diff --git a/examples/hugging-face/image-to-image-async.py b/examples/hugging-face/image-to-image-async.py index 38492f4..d555a7d 100644 --- a/examples/hugging-face/image-to-image-async.py +++ b/examples/hugging-face/image-to-image-async.py @@ -1,18 +1,19 @@ import asyncio +import argparse from huggingface_hub import AsyncInferenceClient from scope3ai import Scope3AI from scope3ai.tracers.huggingface.image_to_image import HUGGING_FACE_IMAGE_TO_IMAGE_TASK -async def main(): +async def main(image_path, model): client = AsyncInferenceClient() scope3 = Scope3AI.init() - model = await client.get_recommended_model(HUGGING_FACE_IMAGE_TO_IMAGE_TASK) + if not model: + model = await client.get_recommended_model(HUGGING_FACE_IMAGE_TO_IMAGE_TASK) with scope3.trace() as tracer: # Replace `image_path` with the path to your image file - image_path = "path/to/your/image.jpg" with open(image_path, "rb") as f: response = await client.image_to_image( model=model, @@ -28,4 +29,16 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) + parser = argparse.ArgumentParser( + description="Run image-to-image transformation with environmental impact tracing." + ) + parser.add_argument("--image-path", type=str, help="Path to the input image file.") + parser.add_argument( + "--model", + type=str, + default=None, + help="Hugging Face model to use. Defaults to the recommended model.", + ) + args = parser.parse_args() + + asyncio.run(main(args.image_path, args.model)) diff --git a/examples/hugging-face/image-to-image.py b/examples/hugging-face/image-to-image.py index 7fdd887..b3fb562 100644 --- a/examples/hugging-face/image-to-image.py +++ b/examples/hugging-face/image-to-image.py @@ -1,18 +1,20 @@ import asyncio +import argparse from huggingface_hub import InferenceClient from scope3ai import Scope3AI from scope3ai.tracers.huggingface.image_to_image import HUGGING_FACE_IMAGE_TO_IMAGE_TASK -async def main(): +async def main(image_path, model): client = InferenceClient() scope3 = Scope3AI.init() - model = client.get_recommended_model(HUGGING_FACE_IMAGE_TO_IMAGE_TASK) + + # Use recommended model if none is provided + if not model: + model = client.get_recommended_model(HUGGING_FACE_IMAGE_TO_IMAGE_TASK) with scope3.trace() as tracer: - # Replace `image_path` with the path to your image file - image_path = "path/to/your/image.jpg" with open(image_path, "rb") as f: response = client.image_to_image( model=model, @@ -28,4 +30,16 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) + parser = argparse.ArgumentParser( + description="Run image-to-image transformation with environmental impact tracing." + ) + parser.add_argument("--image-path", type=str, help="Path to the input image file.") + parser.add_argument( + "--model", + type=str, + default=None, + help="Hugging Face model to use. Defaults to the recommended model.", + ) + args = parser.parse_args() + + asyncio.run(main(args.image_path, args.model)) diff --git a/examples/hugging-face/speech-to-text-async.py b/examples/hugging-face/speech-to-text-async.py index 345965f..b0b5e3f 100644 --- a/examples/hugging-face/speech-to-text-async.py +++ b/examples/hugging-face/speech-to-text-async.py @@ -1,3 +1,4 @@ +import argparse import asyncio from huggingface_hub import AsyncInferenceClient @@ -5,14 +6,14 @@ from scope3ai.tracers.huggingface.speech_to_text import HUGGING_FACE_SPEECH_TO_TEXT_TASK -async def main(): +async def main(audio_path, model): client = AsyncInferenceClient() scope3 = Scope3AI.init() - model = await client.get_recommended_model(HUGGING_FACE_SPEECH_TO_TEXT_TASK) + if not model: + model = await client.get_recommended_model(HUGGING_FACE_SPEECH_TO_TEXT_TASK) with scope3.trace() as tracer: # Replace `audio_path` with the path to your audio file - audio_path = "path/to/your/audio.wav" with open(audio_path, "rb") as f: response = await client.automatic_speech_recognition( model=model, @@ -27,4 +28,16 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) + parser = argparse.ArgumentParser( + description="Run speech-to-text transformation with environmental impact tracing." + ) + parser.add_argument("--audio_path", type=str, help="Path to the input audio file.") + parser.add_argument( + "--model", + type=str, + default=None, + help="Hugging Face model to use. Defaults to the recommended model.", + ) + args = parser.parse_args() + + asyncio.run(main(args.audio_path, args.model)) diff --git a/examples/hugging-face/speech-to-text.py b/examples/hugging-face/speech-to-text.py index 81ce754..9b9474b 100644 --- a/examples/hugging-face/speech-to-text.py +++ b/examples/hugging-face/speech-to-text.py @@ -1,3 +1,4 @@ +import argparse import asyncio from huggingface_hub import InferenceClient @@ -5,14 +6,14 @@ from scope3ai.tracers.huggingface.speech_to_text import HUGGING_FACE_SPEECH_TO_TEXT_TASK -async def main(): +async def main(audio_path, model): client = InferenceClient() scope3 = Scope3AI.init() - model = client.get_recommended_model(HUGGING_FACE_SPEECH_TO_TEXT_TASK) + if not model: + model = client.get_recommended_model(HUGGING_FACE_SPEECH_TO_TEXT_TASK) with scope3.trace() as tracer: # Replace `audio_path` with the path to your audio file - audio_path = "path/to/your/audio.wav" with open(audio_path, "rb") as f: response = client.automatic_speech_recognition( model=model, @@ -27,4 +28,16 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) + parser = argparse.ArgumentParser( + description="Run speech-to-text transformation with environmental impact tracing." + ) + parser.add_argument("--audio_path", type=str, help="Path to the input audio file.") + parser.add_argument( + "--model", + type=str, + default=None, + help="Hugging Face model to use. Defaults to the recommended model.", + ) + args = parser.parse_args() + + asyncio.run(main(args.audio_path, args.model)) diff --git a/scope3ai/tracers/huggingface/image_to_image.py b/scope3ai/tracers/huggingface/image_to_image.py index c1a5014..99c527a 100644 --- a/scope3ai/tracers/huggingface/image_to_image.py +++ b/scope3ai/tracers/huggingface/image_to_image.py @@ -1,3 +1,4 @@ +import io import time from dataclasses import dataclass from typing import Any, Callable, Optional, Union @@ -41,9 +42,12 @@ def _hugging_face_image_to_image_wrapper( encoder = tiktoken.get_encoding("cl100k_base") prompt = args[1] if len(args) > 1 else kwargs.get("prompt", "") input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0 - input_images = None try: - input_image = Image.open(args[0] if len(args) > 0 else kwargs["image"]) + image_param = args[0] if len(args) > 0 else kwargs["image"] + if type(image_param) is str: + input_image = Image.open(args[0] if len(args) > 0 else kwargs["image"]) + else: + input_image = Image.open(io.BytesIO(image_param)) input_width, input_height = input_image.size input_images = [ ("{width}x{height}".format(width=input_width, height=input_height)) diff --git a/scope3ai/tracers/huggingface/instrument.py b/scope3ai/tracers/huggingface/instrument.py index acd72cf..df26048 100644 --- a/scope3ai/tracers/huggingface/instrument.py +++ b/scope3ai/tracers/huggingface/instrument.py @@ -23,6 +23,18 @@ huggingface_translation_wrapper_non_stream, huggingface_translation_wrapper_async_non_stream, ) +from scope3ai.tracers.huggingface.vision.image_classification import ( + huggingface_image_classification_wrapper, + huggingface_image_classification_wrapper_async, +) +from scope3ai.tracers.huggingface.vision.image_segmentation import ( + huggingface_image_segmentation_wrapper, + huggingface_image_segmentation_wrapper_async, +) +from scope3ai.tracers.huggingface.vision.object_detection import ( + huggingface_object_detection_wrapper, + huggingface_object_detection_wrapper_async, +) class HuggingfaceInstrumentor: @@ -83,6 +95,36 @@ def __init__(self) -> None: "name": "AsyncInferenceClient.image_to_image", "wrapper": huggingface_image_to_image_wrapper_async, }, + { + "module": "huggingface_hub.inference._client", + "name": "InferenceClient.image_classification", + "wrapper": huggingface_image_classification_wrapper, + }, + { + "module": "huggingface_hub.inference._generated._async_client", + "name": "AsyncInferenceClient.image_classification", + "wrapper": huggingface_image_classification_wrapper_async, + }, + { + "module": "huggingface_hub.inference._client", + "name": "InferenceClient.image_segmentation", + "wrapper": huggingface_image_segmentation_wrapper, + }, + { + "module": "huggingface_hub.inference._generated._async_client", + "name": "AsyncInferenceClient.image_segmentation", + "wrapper": huggingface_image_segmentation_wrapper_async, + }, + { + "module": "huggingface_hub.inference._client", + "name": "InferenceClient.object_detection", + "wrapper": huggingface_object_detection_wrapper, + }, + { + "module": "huggingface_hub.inference._generated._async_client", + "name": "AsyncInferenceClient.object_detection", + "wrapper": huggingface_object_detection_wrapper_async, + }, ] def instrument(self) -> None: diff --git a/scope3ai/tracers/huggingface/vision/image_classification.py b/scope3ai/tracers/huggingface/vision/image_classification.py new file mode 100644 index 0000000..e4a40c1 --- /dev/null +++ b/scope3ai/tracers/huggingface/vision/image_classification.py @@ -0,0 +1,106 @@ +import io +import time +from dataclasses import dataclass +from typing import Any, Callable, Optional, Union, List +from PIL import Image +from aiohttp import ClientResponse +from huggingface_hub import ( + InferenceClient, + AsyncInferenceClient, + ImageClassificationOutputElement, +) # type: ignore[import-untyped] +from requests import Response + +from scope3ai.api.types import Scope3AIContext, Model, ImpactRow +from scope3ai.api.typesgen import Task +from scope3ai.constants import PROVIDERS +from scope3ai.lib import Scope3AI +from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture +from scope3ai.response_interceptor.requests_interceptor import requests_response_capture + +PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value +HUGGING_FACE_IMAGE_CLASSIFICATION_TASK = "image-classification" + + +@dataclass +class ImageClassificationOutput: + elements: List[ImageClassificationOutputElement] = None + scope3ai: Optional[Scope3AIContext] = None + + +def _hugging_face_image_classification_wrapper( + timer_start: Any, + model: Any, + response: Any, + http_response: Union[ClientResponse, Response], + args: Any, + kwargs: Any, +) -> ImageClassificationOutput: + input_tokens = 0 + if http_response: + compute_time = http_response.headers.get("x-compute-time") + else: + compute_time = time.perf_counter() - timer_start + try: + image_param = args[0] if len(args) > 0 else kwargs["image"] + if type(image_param) is str: + input_image = Image.open(args[0] if len(args) > 0 else kwargs["image"]) + else: + input_image = Image.open(io.BytesIO(image_param)) + input_width, input_height = input_image.size + input_images = [ + ("{width}x{height}".format(width=input_width, height=input_height)) + ] + except Exception: + pass + scope3_row = ImpactRow( + model=Model(id=model), + input_tokens=input_tokens, + task=Task.image_classification, + output_images=[], # No images to output in classification + request_duration_ms=float(compute_time) * 1000, + managed_service_id=PROVIDER, + input_images=input_images, + ) + + scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row) + result = ImageClassificationOutput() + result.elements = response + result.scope3ai = scope3_ctx + return result + + +def huggingface_image_classification_wrapper( + wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any +) -> ImageClassificationOutput: + timer_start = time.perf_counter() + http_response: Response | None = None + with requests_response_capture() as responses: + response = wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_IMAGE_CLASSIFICATION_TASK + ) + return _hugging_face_image_classification_wrapper( + timer_start, model, response, http_response, args, kwargs + ) + + +async def huggingface_image_classification_wrapper_async( + wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any +) -> ImageClassificationOutput: + timer_start = time.perf_counter() + http_response: ClientResponse | None = None + with aiohttp_response_capture() as responses: + response = await wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_IMAGE_CLASSIFICATION_TASK + ) + return _hugging_face_image_classification_wrapper( + timer_start, model, response, http_response, args, kwargs + ) diff --git a/scope3ai/tracers/huggingface/vision/image_segmentation.py b/scope3ai/tracers/huggingface/vision/image_segmentation.py new file mode 100644 index 0000000..b3c9777 --- /dev/null +++ b/scope3ai/tracers/huggingface/vision/image_segmentation.py @@ -0,0 +1,106 @@ +import io +import time +from dataclasses import dataclass +from typing import Any, Callable, Optional, Union, List +from PIL import Image + +from aiohttp import ClientResponse +from huggingface_hub import ( + InferenceClient, + AsyncInferenceClient, + ImageSegmentationOutputElement, +) # type: ignore[import-untyped] +from requests import Response + +from scope3ai.api.types import Scope3AIContext, Model, ImpactRow +from scope3ai.api.typesgen import Task +from scope3ai.constants import PROVIDERS +from scope3ai.lib import Scope3AI +from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture +from scope3ai.response_interceptor.requests_interceptor import requests_response_capture + +PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value +HUGGING_FACE_IMAGE_SEGMENTATION_TASK = "image-segmentation" + + +@dataclass +class ImageSegmentationOutput: + elements: List[ImageSegmentationOutputElement] = None + scope3ai: Optional[Scope3AIContext] = None + + +def _hugging_face_image_segmentation_wrapper( + timer_start: Any, + model: Any, + response: Any, + http_response: Union[ClientResponse, Response], + args: Any, + kwargs: Any, +) -> ImageSegmentationOutput: + input_tokens = 0 + if http_response: + compute_time = http_response.headers.get("x-compute-time") + else: + compute_time = time.perf_counter() - timer_start + input_images = None + try: + image_param = args[0] if len(args) > 0 else kwargs["image"] + if type(image_param) is str: + input_image = Image.open(args[0] if len(args) > 0 else kwargs["image"]) + else: + input_image = Image.open(io.BytesIO(image_param)) + input_width, input_height = input_image.size + input_images = [ + ("{width}x{height}".format(width=input_width, height=input_height)) + ] + except Exception: + pass + scope3_row = ImpactRow( + model=Model(id=model), + input_tokens=input_tokens, + task=Task.image_segmentation, + request_duration_ms=float(compute_time) * 1000, + managed_service_id=PROVIDER, + input_images=input_images, + ) + scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row) + result = ImageSegmentationOutput() + result.elements = response + result.scope3ai = scope3_ctx + return result + + +def huggingface_image_segmentation_wrapper( + wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any +) -> ImageSegmentationOutput: + timer_start = time.perf_counter() + http_response: Response | None = None + with requests_response_capture() as responses: + response = wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_IMAGE_SEGMENTATION_TASK + ) + return _hugging_face_image_segmentation_wrapper( + timer_start, model, response, http_response, args, kwargs + ) + + +async def huggingface_image_segmentation_wrapper_async( + wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any +) -> ImageSegmentationOutput: + timer_start = time.perf_counter() + http_response: ClientResponse | None = None + with aiohttp_response_capture() as responses: + response = await wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_IMAGE_SEGMENTATION_TASK + ) + return _hugging_face_image_segmentation_wrapper( + timer_start, model, response, http_response, args, kwargs + ) diff --git a/scope3ai/tracers/huggingface/vision/object_detection.py b/scope3ai/tracers/huggingface/vision/object_detection.py new file mode 100644 index 0000000..71585a0 --- /dev/null +++ b/scope3ai/tracers/huggingface/vision/object_detection.py @@ -0,0 +1,106 @@ +import io +import time +from dataclasses import dataclass +from typing import Any, Callable, Optional, Union, List + +from PIL import Image +from aiohttp import ClientResponse +from huggingface_hub import ( + InferenceClient, + AsyncInferenceClient, + ObjectDetectionOutputElement, +) # type: ignore[import-untyped] +from requests import Response + +from scope3ai.api.types import Scope3AIContext, Model, ImpactRow +from scope3ai.api.typesgen import Task +from scope3ai.constants import PROVIDERS +from scope3ai.lib import Scope3AI +from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture +from scope3ai.response_interceptor.requests_interceptor import requests_response_capture + +PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value +HUGGING_FACE_OBJECT_DETECTION_TASK = "object-detection" + + +@dataclass +class ObjectDetectionOutput: + elements: List[ObjectDetectionOutputElement] = None + scope3ai: Optional[Scope3AIContext] = None + + +def _hugging_face_object_detection_wrapper( + timer_start: Any, + model: Any, + response: Any, + http_response: Union[ClientResponse, Response], + args: Any, + kwargs: Any, +) -> ObjectDetectionOutput: + input_tokens = 0 + if http_response: + compute_time = http_response.headers.get("x-compute-time") + else: + compute_time = time.perf_counter() - timer_start + try: + image_param = args[0] if len(args) > 0 else kwargs["image"] + if type(image_param) is str: + input_image = Image.open(args[0] if len(args) > 0 else kwargs["image"]) + else: + input_image = Image.open(io.BytesIO(image_param)) + input_width, input_height = input_image.size + input_images = [ + ("{width}x{height}".format(width=input_width, height=input_height)) + ] + except Exception: + pass + scope3_row = ImpactRow( + model=Model(id=model), + input_tokens=input_tokens, + task=Task.object_detection, + request_duration_ms=float(compute_time) * 1000, + managed_service_id=PROVIDER, + input_images=input_images, + ) + + scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row) + result = ObjectDetectionOutput() + result.elements = response + result.scope3ai = scope3_ctx + return result + + +def huggingface_object_detection_wrapper( + wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any +) -> ObjectDetectionOutput: + timer_start = time.perf_counter() + http_response: Response | None = None + with requests_response_capture() as responses: + response = wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_OBJECT_DETECTION_TASK + ) + return _hugging_face_object_detection_wrapper( + timer_start, model, response, http_response, args, kwargs + ) + + +async def huggingface_object_detection_wrapper_async( + wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any +) -> ObjectDetectionOutput: + timer_start = time.perf_counter() + http_response: ClientResponse | None = None + with aiohttp_response_capture() as responses: + response = await wrapped(*args, **kwargs) + http_responses = responses.get() + if len(http_responses) > 0: + http_response = http_responses[-1] + model = kwargs.get("model") or instance.get_recommended_model( + HUGGING_FACE_OBJECT_DETECTION_TASK + ) + return _hugging_face_object_detection_wrapper( + timer_start, model, response, http_response, args, kwargs + ) diff --git a/tests/cassettes/test_huggingface_hub_image_classification.yaml b/tests/cassettes/test_huggingface_hub_image_classification.yaml new file mode 100644 index 0000000..b68ad75 --- /dev/null +++ b/tests/cassettes/test_huggingface_hub_image_classification.yaml @@ -0,0 +1,18467 @@ +interactions: +- 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Can be used to train + `feature-extraction` models.\",\"id\":\"wikipedia\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"India, + officially the Republic of India, is a country in South Asia.\",\"type\":\"text\"}],\"outputs\":[{\"table\":[[\"Dimension + 1\",\"Dimension 2\",\"Dimension 3\"],[\"2.583383083343506\",\"2.757075071334839\",\"0.9023529887199402\"],[\"8.29393482208252\",\"1.1071064472198486\",\"2.03399395942688\"],[\"-0.7754912972450256\",\"-1.647324562072754\",\"-0.6113331913948059\"],[\"0.07087723910808563\",\"1.5942802429199219\",\"1.4610432386398315\"]],\"type\":\"tabular\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + powerful feature extraction model for natural language processing tasks.\",\"id\":\"thenlper/gte-large\"},{\"description\":\"A + strong feature extraction model for retrieval.\",\"id\":\"Alibaba-NLP/gte-Qwen1.5-7B-instruct\"}],\"spaces\":[{\"description\":\"A + leaderboard to rank text feature extraction models based on a benchmark.\",\"id\":\"mteb/leaderboard\"},{\"description\":\"A + leaderboard to rank best feature extraction models based on human feedback.\",\"id\":\"mteb/arena\"}],\"summary\":\"Feature + extraction is the task of extracting features learnt in a model.\",\"widgetModels\":[\"facebook/bart-base\"],\"id\":\"feature-extraction\",\"label\":\"Feature + Extraction\",\"libraries\":[\"sentence-transformers\",\"transformers\",\"transformers.js\"]},\"fill-mask\":{\"datasets\":[{\"description\":\"A + common dataset that is used to train models for many languages.\",\"id\":\"wikipedia\"},{\"description\":\"A + large English dataset with text crawled from the web.\",\"id\":\"c4\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"The + barked at me\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"wolf\",\"score\":0.487},{\"label\":\"dog\",\"score\":0.061},{\"label\":\"cat\",\"score\":0.058},{\"label\":\"fox\",\"score\":0.047},{\"label\":\"squirrel\",\"score\":0.025}]}]},\"metrics\":[{\"description\":\"Cross + Entropy is a metric that calculates the difference between two probability + distributions. Each probability distribution is the distribution of predicted + words\",\"id\":\"cross_entropy\"},{\"description\":\"Perplexity is the exponential + of the cross-entropy loss. It evaluates the probabilities assigned to the + next word by the model. Lower perplexity indicates better performance\",\"id\":\"perplexity\"}],\"models\":[{\"description\":\"The + famous BERT model.\",\"id\":\"google-bert/bert-base-uncased\"},{\"description\":\"A + multilingual model trained on 100 languages.\",\"id\":\"FacebookAI/xlm-roberta-base\"}],\"spaces\":[],\"summary\":\"Masked + language modeling is the task of masking some of the words in a sentence and + predicting which words should replace those masks. These models are useful + when we want to get a statistical understanding of the language in which the + model is trained in.\",\"widgetModels\":[\"distilroberta-base\"],\"youtubeId\":\"mqElG5QJWUg\",\"id\":\"fill-mask\",\"label\":\"Fill-Mask\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"image-classification\":{\"datasets\":[{\"description\":\"Benchmark + dataset used for image classification with images that belong to 100 classes.\",\"id\":\"cifar100\"},{\"description\":\"Dataset + consisting of images of garments.\",\"id\":\"fashion_mnist\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-classification-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Egyptian + cat\",\"score\":0.514},{\"label\":\"Tabby cat\",\"score\":0.193},{\"label\":\"Tiger + cat\",\"score\":0.068}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + strong image classification model.\",\"id\":\"google/vit-base-patch16-224\"},{\"description\":\"A + robust image classification model.\",\"id\":\"facebook/deit-base-distilled-patch16-224\"},{\"description\":\"A + strong image classification model.\",\"id\":\"facebook/convnext-large-224\"}],\"spaces\":[{\"description\":\"An + application that classifies what a given image is about.\",\"id\":\"nielsr/perceiver-image-classification\"}],\"summary\":\"Image + classification is the task of assigning a label or class to an entire image. + Images are expected to have only one class for each image. Image classification + models take an image as input and return a prediction about which class the + image belongs to.\",\"widgetModels\":[\"google/vit-base-patch16-224\"],\"youtubeId\":\"tjAIM7BOYhw\",\"id\":\"image-classification\",\"label\":\"Image + Classification\",\"libraries\":[\"keras\",\"timm\",\"transformers\",\"transformers.js\"]},\"image-feature-extraction\":{\"datasets\":[{\"description\":\"ImageNet-1K + is a image classification dataset in which images are used to train image-feature-extraction + models.\",\"id\":\"imagenet-1k\"}],\"demo\":{\"inputs\":[{\"filename\":\"mask-generation-input.png\",\"type\":\"img\"}],\"outputs\":[{\"table\":[[\"Dimension + 1\",\"Dimension 2\",\"Dimension 3\"],[\"0.21236686408519745\",\"1.0919708013534546\",\"0.8512550592422485\"],[\"0.809657871723175\",\"-0.18544459342956543\",\"-0.7851548194885254\"],[\"1.3103108406066895\",\"-0.2479034662246704\",\"-0.9107287526130676\"],[\"1.8536205291748047\",\"-0.36419737339019775\",\"0.09717650711536407\"]],\"type\":\"tabular\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + powerful image feature extraction model.\",\"id\":\"timm/vit_large_patch14_dinov2.lvd142m\"},{\"description\":\"A + strong image feature extraction model.\",\"id\":\"nvidia/MambaVision-T-1K\"},{\"description\":\"A + robust image feature extraction model.\",\"id\":\"facebook/dino-vitb16\"},{\"description\":\"Strong + image feature extraction model made for information retrieval from documents.\",\"id\":\"vidore/colpali\"},{\"description\":\"Strong + image feature extraction model that can be used on images and documents.\",\"id\":\"OpenGVLab/InternViT-6B-448px-V1-2\"}],\"spaces\":[],\"summary\":\"Image + feature extraction is the task of extracting features learnt in a computer + vision model.\",\"widgetModels\":[],\"id\":\"image-feature-extraction\",\"label\":\"Image + Feature Extraction\",\"libraries\":[\"timm\",\"transformers\"]},\"image-segmentation\":{\"datasets\":[{\"description\":\"Scene + segmentation dataset.\",\"id\":\"scene_parse_150\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-segmentation-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"image-segmentation-output.png\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"Average + Precision (AP) is the Area Under the PR Curve (AUC-PR). It is calculated for + each semantic class separately\",\"id\":\"Average Precision\"},{\"description\":\"Mean + Average Precision (mAP) is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"Intersection over Union (IoU) is the + overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic + classes\",\"id\":\"Mean Intersection over Union\"},{\"description\":\"AP\u03B1 + is the Average Precision at the IoU threshold of a \u03B1 value, for example, + AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + semantic segmentation model trained on ADE20k.\",\"id\":\"openmmlab/upernet-convnext-small\"},{\"description\":\"Background + removal model.\",\"id\":\"briaai/RMBG-1.4\"},{\"description\":\"A multipurpose + image segmentation model for high resolution images.\",\"id\":\"ZhengPeng7/BiRefNet\"},{\"description\":\"Powerful + human-centric image segmentation model.\",\"id\":\"facebook/sapiens-seg-1b\"},{\"description\":\"Panoptic + segmentation model trained on the COCO (common objects) dataset.\",\"id\":\"facebook/mask2former-swin-large-coco-panoptic\"}],\"spaces\":[{\"description\":\"A + semantic segmentation application that can predict unseen instances out of + the box.\",\"id\":\"facebook/ov-seg\"},{\"description\":\"One of the strongest + segmentation applications.\",\"id\":\"jbrinkma/segment-anything\"},{\"description\":\"A + human-centric segmentation model.\",\"id\":\"facebook/sapiens-pose\"},{\"description\":\"An + instance segmentation application to predict neuronal cell types from microscopy + images.\",\"id\":\"rashmi/sartorius-cell-instance-segmentation\"},{\"description\":\"An + application that segments videos.\",\"id\":\"ArtGAN/Segment-Anything-Video\"},{\"description\":\"An + panoptic segmentation application built for outdoor environments.\",\"id\":\"segments/panoptic-segment-anything\"}],\"summary\":\"Image + Segmentation divides an image into segments where each pixel in the image + is mapped to an object. This task has multiple variants such as instance segmentation, + panoptic segmentation and semantic segmentation.\",\"widgetModels\":[\"nvidia/segformer-b0-finetuned-ade-512-512\"],\"youtubeId\":\"dKE8SIt9C-w\",\"id\":\"image-segmentation\",\"label\":\"Image + Segmentation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"image-to-image\":{\"datasets\":[{\"description\":\"Synthetic + dataset, for image relighting\",\"id\":\"VIDIT\"},{\"description\":\"Multiple + images of celebrities, used for facial expression translation\",\"id\":\"huggan/CelebA-faces\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-image-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"image-to-image-output.png\",\"type\":\"img\"}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"Peak + Signal to Noise Ratio (PSNR) is an approximation of the human perception, + considering the ratio of the absolute intensity with respect to the variations. + Measured in dB, a high value indicates a high fidelity.\",\"id\":\"PSNR\"},{\"description\":\"Structural + Similarity Index (SSIM) is a perceptual metric which compares the luminance, + contrast and structure of two images. The values of SSIM range between -1 + and 1, and higher values indicate closer resemblance to the original image.\",\"id\":\"SSIM\"},{\"description\":\"Inception + Score (IS) is an analysis of the labels predicted by an image classification + model when presented with a sample of the generated images.\",\"id\":\"IS\"}],\"models\":[{\"description\":\"An + image-to-image model to improve image resolution.\",\"id\":\"fal/AuraSR-v2\"},{\"description\":\"A + model that increases the resolution of an image.\",\"id\":\"keras-io/super-resolution\"},{\"description\":\"A + model that creates a set of variations of the input image in the style of + DALL-E using Stable Diffusion.\",\"id\":\"lambdalabs/sd-image-variations-diffusers\"},{\"description\":\"A + model that generates images based on segments in the input image and the text + prompt.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"A + model that takes an image and an instruction to edit the image.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"spaces\":[{\"description\":\"Image + enhancer application for low light.\",\"id\":\"keras-io/low-light-image-enhancement\"},{\"description\":\"Style + transfer application.\",\"id\":\"keras-io/neural-style-transfer\"},{\"description\":\"An + application that generates images based on segment control.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"Image + generation application that takes image control and text prompt.\",\"id\":\"hysts/ControlNet\"},{\"description\":\"Colorize + any image using this app.\",\"id\":\"ioclab/brightness-controlnet\"},{\"description\":\"Edit + images with instructions.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"summary\":\"Image-to-image + is the task of transforming an input image through a variety of possible manipulations + and enhancements, such as super-resolution, image inpainting, colorization, + and more.\",\"widgetModels\":[\"stabilityai/stable-diffusion-2-inpainting\"],\"youtubeId\":\"\",\"id\":\"image-to-image\",\"label\":\"Image-to-Image\",\"libraries\":[\"diffusers\",\"transformers\",\"transformers.js\"]},\"image-text-to-text\":{\"datasets\":[{\"description\":\"Instructions + composed of image and text.\",\"id\":\"liuhaotian/LLaVA-Instruct-150K\"},{\"description\":\"Conversation + turns where questions involve image and text.\",\"id\":\"liuhaotian/LLaVA-Pretrain\"},{\"description\":\"A + collection of datasets made for model fine-tuning.\",\"id\":\"HuggingFaceM4/the_cauldron\"},{\"description\":\"Screenshots + of websites with their HTML/CSS codes.\",\"id\":\"HuggingFaceM4/WebSight\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-text-to-text-input.png\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"Describe the position of the bee in detail.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + bee is sitting on a pink flower, surrounded by other flowers. The bee is positioned + in the center of the flower, with its head and front legs sticking out.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + vision language model with great visual understanding and reasoning capabilities.\",\"id\":\"meta-llama/Llama-3.2-11B-Vision-Instruct\"},{\"description\":\"Cutting-edge + vision language models.\",\"id\":\"allenai/Molmo-7B-D-0924\"},{\"description\":\"Small + yet powerful model.\",\"id\":\"vikhyatk/moondream2\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"Qwen/Qwen2-VL-7B-Instruct\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"mistralai/Pixtral-12B-2409\"},{\"description\":\"Strong + image-text-to-text model focused on documents.\",\"id\":\"stepfun-ai/GOT-OCR2_0\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate vision language models.\",\"id\":\"opencompass/open_vlm_leaderboard\"},{\"description\":\"Vision + language models arena, where models are ranked by votes of users.\",\"id\":\"WildVision/vision-arena\"},{\"description\":\"Powerful + vision-language model assistant.\",\"id\":\"akhaliq/Molmo-7B-D-0924\"},{\"description\":\"An + image-text-to-text application focused on documents.\",\"id\":\"stepfun-ai/GOT_official_online_demo\"},{\"description\":\"An + application to compare outputs of different vision language models.\",\"id\":\"merve/compare_VLMs\"},{\"description\":\"An + application for chatting with an image-text-to-text model.\",\"id\":\"GanymedeNil/Qwen2-VL-7B\"}],\"summary\":\"Image-text-to-text + models take in an image and text prompt and output text. These models are + also called vision-language models, or VLMs. The difference from image-to-text + models is that these models take an additional text input, not restricting + the model to certain use cases like image captioning, and may also be trained + to accept a conversation as input.\",\"widgetModels\":[\"meta-llama/Llama-3.2-11B-Vision-Instruct\"],\"youtubeId\":\"IoGaGfU1CIg\",\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"libraries\":[\"transformers\"]},\"image-to-text\":{\"datasets\":[{\"description\":\"Dataset + from 12M image-text of Reddit\",\"id\":\"red_caps\"},{\"description\":\"Dataset + from 3.3M images of Google\",\"id\":\"datasets/conceptual_captions\"}],\"demo\":{\"inputs\":[{\"filename\":\"savanna.jpg\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Detailed + description\",\"content\":\"a herd of giraffes and zebras grazing in a field\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust image captioning model.\",\"id\":\"Salesforce/blip2-opt-2.7b\"},{\"description\":\"A + powerful and accurate image-to-text model that can also localize concepts + in images.\",\"id\":\"microsoft/kosmos-2-patch14-224\"},{\"description\":\"A + strong optical character recognition model.\",\"id\":\"facebook/nougat-base\"},{\"description\":\"A + powerful model that lets you have a conversation with the image.\",\"id\":\"llava-hf/llava-1.5-7b-hf\"}],\"spaces\":[{\"description\":\"An + application that compares various image captioning models.\",\"id\":\"nielsr/comparing-captioning-models\"},{\"description\":\"A + robust image captioning application.\",\"id\":\"flax-community/image-captioning\"},{\"description\":\"An + application that transcribes handwritings into text.\",\"id\":\"nielsr/TrOCR-handwritten\"},{\"description\":\"An + application that can caption images and answer questions about a given image.\",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An + application that can caption images and answer questions with a conversational + agent.\",\"id\":\"Salesforce/BLIP2\"},{\"description\":\"An image captioning + application that demonstrates the effect of noise on captions.\",\"id\":\"johko/capdec-image-captioning\"}],\"summary\":\"Image + to text models output a text from a given image. Image captioning or optical + character recognition can be considered as the most common applications of + image to text.\",\"widgetModels\":[\"Salesforce/blip-image-captioning-large\"],\"youtubeId\":\"\",\"id\":\"image-to-text\",\"label\":\"Image-to-Text\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"keypoint-detection\":{\"datasets\":[{\"description\":\"A + dataset of hand keypoints of over 500k examples.\",\"id\":\"Vincent-luo/hagrid-mediapipe-hands\"}],\"demo\":{\"inputs\":[{\"filename\":\"keypoint-detection-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"keypoint-detection-output.png\",\"type\":\"img\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust keypoint detection model.\",\"id\":\"magic-leap-community/superpoint\"},{\"description\":\"Strong + keypoint detection model used to detect human pose.\",\"id\":\"facebook/sapiens-pose-1b\"}],\"spaces\":[{\"description\":\"An + application that detects hand keypoints in real-time.\",\"id\":\"datasciencedojo/Hand-Keypoint-Detection-Realtime\"},{\"description\":\"An + application to try a universal keypoint detection model.\",\"id\":\"merve/SuperPoint\"}],\"summary\":\"Keypoint + detection is the task of identifying meaningful distinctive points or features + in an image.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"keypoint-detection\",\"label\":\"Keypoint + Detection\",\"libraries\":[\"transformers\"]},\"mask-generation\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple Vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Medical + Imaging dataset of the Human Brain for segmentation and mask generating tasks\",\"id\":\"rocky93/BraTS_segmentation\"}],\"demo\":{\"inputs\":[{\"filename\":\"mask-generation-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"mask-generation-output.png\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"IoU + is used to measure the overlap between predicted mask and the ground truth + mask.\",\"id\":\"Intersection over Union (IoU)\"}],\"models\":[{\"description\":\"Small + yet powerful mask generation model.\",\"id\":\"Zigeng/SlimSAM-uniform-50\"},{\"description\":\"Very + strong mask generation model.\",\"id\":\"facebook/sam2-hiera-large\"}],\"spaces\":[{\"description\":\"An + application that combines a mask generation model with a zero-shot object + detection model for text-guided image segmentation.\",\"id\":\"merve/OWLSAM2\"},{\"description\":\"An + application that compares the performance of a large and a small mask generation + model.\",\"id\":\"merve/slimsam\"},{\"description\":\"An application based + on an improved mask generation model.\",\"id\":\"SkalskiP/segment-anything-model-2\"},{\"description\":\"An + application to remove objects from videos using mask generation models.\",\"id\":\"SkalskiP/SAM_and_ProPainter\"}],\"summary\":\"Mask + generation is the task of generating masks that identify a specific object + or region of interest in a given image. Masks are often used in segmentation + tasks, where they provide a precise way to isolate the object of interest + for further processing or analysis.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"mask-generation\",\"label\":\"Mask + Generation\",\"libraries\":[\"transformers\"]},\"object-detection\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Multi-task + computer vision benchmark.\",\"id\":\"merve/pascal-voc\"}],\"demo\":{\"inputs\":[{\"filename\":\"object-detection-input.jpg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + object detection model pre-trained on the COCO 2017 dataset.\",\"id\":\"facebook/detr-resnet-50\"},{\"description\":\"Real-time + and accurate object detection model.\",\"id\":\"jameslahm/yolov10x\"},{\"description\":\"Fast + and accurate object detection model trained on COCO and Object365 datasets.\",\"id\":\"PekingU/rtdetr_r18vd_coco_o365\"}],\"spaces\":[{\"description\":\"Leaderboard + to compare various object detection models across several metrics.\",\"id\":\"hf-vision/object_detection_leaderboard\"},{\"description\":\"An + application that contains various object detection models to try from.\",\"id\":\"Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS\"},{\"description\":\"An + application that shows multiple cutting edge techniques for object detection + and tracking.\",\"id\":\"kadirnar/torchyolo\"},{\"description\":\"An object + tracking, segmentation and inpainting application.\",\"id\":\"VIPLab/Track-Anything\"},{\"description\":\"Very + fast object tracking application based on object detection.\",\"id\":\"merve/RT-DETR-tracking-coco\"}],\"summary\":\"Object + Detection models allow users to identify objects of certain defined classes. + Object detection models receive an image as input and output the images with + bounding boxes and labels on detected objects.\",\"widgetModels\":[\"facebook/detr-resnet-50\"],\"youtubeId\":\"WdAeKSOpxhw\",\"id\":\"object-detection\",\"label\":\"Object + Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"video-classification\":{\"datasets\":[{\"description\":\"Benchmark + dataset used for video classification with videos that belong to 400 classes.\",\"id\":\"kinetics400\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-classification-input.gif\",\"type\":\"img\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Playing + Guitar\",\"score\":0.514},{\"label\":\"Playing Tennis\",\"score\":0.193},{\"label\":\"Cooking\",\"score\":0.068}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"google/vivit-b-16x2-kinetics400\"},{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"microsoft/xclip-base-patch32\"}],\"spaces\":[{\"description\":\"An + application that classifies video at different timestamps.\",\"id\":\"nateraw/lavila\"},{\"description\":\"An + application that classifies video.\",\"id\":\"fcakyon/video-classification\"}],\"summary\":\"Video + classification is the task of assigning a label or class to an entire video. + Videos are expected to have only one class for each video. Video classification + models take a video as input and return a prediction about which class the + video belongs to.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"video-classification\",\"label\":\"Video + Classification\",\"libraries\":[\"transformers\"]},\"question-answering\":{\"datasets\":[{\"description\":\"A + famous question answering dataset based on English articles from Wikipedia.\",\"id\":\"squad_v2\"},{\"description\":\"A + dataset of aggregated anonymized actual queries issued to the Google search + engine.\",\"id\":\"natural_questions\"}],\"demo\":{\"inputs\":[{\"label\":\"Question\",\"content\":\"Which + name is also used to describe the Amazon rainforest in English?\",\"type\":\"text\"},{\"label\":\"Context\",\"content\":\"The + Amazon rainforest, also known in English as Amazonia or the Amazon Jungle\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"Amazonia\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Exact + Match is a metric based on the strict character match of the predicted answer + and the right answer. For answers predicted correctly, the Exact Match will + be 1. Even if only one character is different, Exact Match will be 0\",\"id\":\"exact-match\"},{\"description\":\" + The F1-Score metric is useful if we value both false positives and false negatives + equally. The F1-Score is calculated on each word in the predicted sequence + against the correct answer\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust baseline model for most question answering domains.\",\"id\":\"deepset/roberta-base-squad2\"},{\"description\":\"Small + yet robust model that can answer questions.\",\"id\":\"distilbert/distilbert-base-cased-distilled-squad\"},{\"description\":\"A + special model that can answer questions from tables.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that can answer a long question from Wikipedia.\",\"id\":\"deepset/wikipedia-assistant\"}],\"summary\":\"Question + Answering models can retrieve the answer to a question from a given text, + which is useful for searching for an answer in a document. Some question answering + models can generate answers without context!\",\"widgetModels\":[\"deepset/roberta-base-squad2\"],\"youtubeId\":\"ajPx5LwJD-I\",\"id\":\"question-answering\",\"label\":\"Question + Answering\",\"libraries\":[\"adapter-transformers\",\"allennlp\",\"transformers\",\"transformers.js\"]},\"reinforcement-learning\":{\"datasets\":[{\"description\":\"A + curation of widely used datasets for Data Driven Deep Reinforcement Learning + (D4RL)\",\"id\":\"edbeeching/decision_transformer_gym_replay\"}],\"demo\":{\"inputs\":[{\"label\":\"State\",\"content\":\"Red + traffic light, pedestrians are about to pass.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Action\",\"content\":\"Stop + the car.\",\"type\":\"text\"},{\"label\":\"Next State\",\"content\":\"Yellow + light, pedestrians have crossed.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Accumulated + reward across all time steps discounted by a factor that ranges between 0 + and 1 and determines how much the agent optimizes for future relative to immediate + rewards. Measures how good is the policy ultimately found by a given algorithm + considering uncertainty over the future.\",\"id\":\"Discounted Total Reward\"},{\"description\":\"Average + return obtained after running the policy for a certain number of evaluation + episodes. As opposed to total reward, mean reward considers how much reward + a given algorithm receives while learning.\",\"id\":\"Mean Reward\"},{\"description\":\"Measures + how good a given algorithm is after a predefined time. Some algorithms may + be guaranteed to converge to optimal behavior across many time steps. However, + an agent that reaches an acceptable level of optimality after a given time + horizon may be preferable to one that ultimately reaches optimality but takes + a long time.\",\"id\":\"Level of Performance After Some Time\"}],\"models\":[{\"description\":\"A + Reinforcement Learning model trained on expert data from the Gym Hopper environment\",\"id\":\"edbeeching/decision-transformer-gym-hopper-expert\"},{\"description\":\"A + PPO agent playing seals/CartPole-v0 using the stable-baselines3 library and + the RL Zoo.\",\"id\":\"HumanCompatibleAI/ppo-seals-CartPole-v0\"}],\"spaces\":[{\"description\":\"An + application for a cute puppy agent learning to catch a stick.\",\"id\":\"ThomasSimonini/Huggy\"},{\"description\":\"An + application to play Snowball Fight with a reinforcement learning agent.\",\"id\":\"ThomasSimonini/SnowballFight\"}],\"summary\":\"Reinforcement + learning is the computational approach of learning from action by interacting + with an environment through trial and error and receiving rewards (negative + or positive) as feedback\",\"widgetModels\":[],\"youtubeId\":\"q0BiUn5LiBc\",\"id\":\"reinforcement-learning\",\"label\":\"Reinforcement + Learning\",\"libraries\":[\"transformers\",\"stable-baselines3\",\"ml-agents\",\"sample-factory\"]},\"sentence-similarity\":{\"datasets\":[{\"description\":\"Bing + queries with relevant passages from various web sources.\",\"id\":\"ms_marco\"}],\"demo\":{\"inputs\":[{\"label\":\"Source + sentence\",\"content\":\"Machine learning is so easy.\",\"type\":\"text\"},{\"label\":\"Sentences + to compare to\",\"content\":\"Deep learning is so straightforward.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"This + is so difficult, like rocket science.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"I + can't believe how much I struggled with this.\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Deep + learning is so straightforward.\",\"score\":0.623},{\"label\":\"This is so + difficult, like rocket science.\",\"score\":0.413},{\"label\":\"I can't believe + how much I struggled with this.\",\"score\":0.256}]}]},\"metrics\":[{\"description\":\"Reciprocal + Rank is a measure used to rank the relevancy of documents given a set of documents. + Reciprocal Rank is the reciprocal of the rank of the document retrieved, meaning, + if the rank is 3, the Reciprocal Rank is 0.33. If the rank is 1, the Reciprocal + Rank is 1\",\"id\":\"Mean Reciprocal Rank\"},{\"description\":\"The similarity + of the embeddings is evaluated mainly on cosine similarity. It is calculated + as the cosine of the angle between two vectors. It is particularly useful + when your texts are not the same length\",\"id\":\"Cosine Similarity\"}],\"models\":[{\"description\":\"This + model works well for sentences and paragraphs and can be used for clustering/grouping + and semantic searches.\",\"id\":\"sentence-transformers/all-mpnet-base-v2\"},{\"description\":\"A + multilingual robust sentence similarity model..\",\"id\":\"BAAI/bge-m3\"}],\"spaces\":[{\"description\":\"An + application that leverages sentence similarity to answer questions from YouTube + videos.\",\"id\":\"Gradio-Blocks/Ask_Questions_To_YouTube_Videos\"},{\"description\":\"An + application that retrieves relevant PubMed abstracts for a given online article + which can be used as further references.\",\"id\":\"Gradio-Blocks/pubmed-abstract-retriever\"},{\"description\":\"An + application that leverages sentence similarity to summarize text.\",\"id\":\"nickmuchi/article-text-summarizer\"},{\"description\":\"A + guide that explains how Sentence Transformers can be used for semantic search.\",\"id\":\"sentence-transformers/Sentence_Transformers_for_semantic_search\"}],\"summary\":\"Sentence + Similarity is the task of determining how similar two texts are. Sentence + similarity models convert input texts into vectors (embeddings) that capture + semantic information and calculate how close (similar) they are between them. + This task is particularly useful for information retrieval and clustering/grouping.\",\"widgetModels\":[\"BAAI/bge-small-en-v1.5\"],\"youtubeId\":\"VCZq5AkbNEU\",\"id\":\"sentence-similarity\",\"label\":\"Sentence + Similarity\",\"libraries\":[\"sentence-transformers\",\"spacy\",\"transformers.js\"]},\"summarization\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"News + articles in five different languages along with their summaries. Widely used + for benchmarking multilingual summarization models.\",\"id\":\"mlsum\"},{\"description\":\"English + conversations and their summaries. Useful for benchmarking conversational + agents.\",\"id\":\"samsum\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building, and the tallest structure in Paris. Its base is square, measuring + 125 metres (410 ft) on each side. It was the first structure to reach a height + of 300 metres. Excluding transmitters, the Eiffel Tower is the second tallest + free-standing structure in France after the Millau Viaduct.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building. It was the first structure to reach a height of 300 metres.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"The + generated sequence is compared against its summary, and the overlap of tokens + are counted. ROUGE-N refers to overlap of N subsequent tokens, ROUGE-1 refers + to overlap of single tokens and ROUGE-2 is the overlap of two subsequent tokens.\",\"id\":\"rouge\"}],\"models\":[{\"description\":\"A + strong summarization model trained on English news articles. Excels at generating + factual summaries.\",\"id\":\"facebook/bart-large-cnn\"},{\"description\":\"A + summarization model trained on medical articles.\",\"id\":\"Falconsai/medical_summarization\"}],\"spaces\":[{\"description\":\"An + application that can summarize long paragraphs.\",\"id\":\"pszemraj/summarize-long-text\"},{\"description\":\"A + much needed summarization application for terms and conditions.\",\"id\":\"ml6team/distilbart-tos-summarizer-tosdr\"},{\"description\":\"An + application that summarizes long documents.\",\"id\":\"pszemraj/document-summarization\"},{\"description\":\"An + application that can detect errors in abstractive summarization.\",\"id\":\"ml6team/post-processing-summarization\"}],\"summary\":\"Summarization + is the task of producing a shorter version of a document while preserving + its important information. Some models can extract text from the original + input, while other models can generate entirely new text.\",\"widgetModels\":[\"facebook/bart-large-cnn\"],\"youtubeId\":\"yHnr5Dk2zCI\",\"id\":\"summarization\",\"label\":\"Summarization\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"table-question-answering\":{\"datasets\":[{\"description\":\"The + WikiTableQuestions dataset is a large-scale dataset for the task of question + answering on semi-structured tables.\",\"id\":\"wikitablequestions\"},{\"description\":\"WikiSQL + is a dataset of 80654 hand-annotated examples of questions and SQL queries + distributed across 24241 tables from Wikipedia.\",\"id\":\"wikisql\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Rank\",\"Name\",\"No.of + reigns\",\"Combined days\"],[\"1\",\"lou Thesz\",\"3\",\"3749\"],[\"2\",\"Ric + Flair\",\"8\",\"3103\"],[\"3\",\"Harley Race\",\"7\",\"1799\"]],\"type\":\"tabular\"},{\"label\":\"Question\",\"content\":\"What + is the number of reigns for Harley Race?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"7\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Checks + whether the predicted answer(s) is the same as the ground-truth answer(s).\",\"id\":\"Denotation + Accuracy\"}],\"models\":[{\"description\":\"A table question answering model + that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL + query on a given table.\",\"id\":\"microsoft/tapex-base\"},{\"description\":\"A + robust table question answering model.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that answers questions based on table CSV files.\",\"id\":\"katanaml/table-query\"}],\"summary\":\"Table + Question Answering (Table QA) is the answering a question about an information + on a given table.\",\"widgetModels\":[\"google/tapas-base-finetuned-wtq\"],\"id\":\"table-question-answering\",\"label\":\"Table + Question Answering\",\"libraries\":[\"transformers\"]},\"tabular-classification\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Glucose\",\"Blood + Pressure \",\"Skin Thickness\",\"Insulin\",\"BMI\"],[\"148\",\"72\",\"35\",\"0\",\"33.6\"],[\"150\",\"50\",\"30\",\"0\",\"35.1\"],[\"141\",\"60\",\"29\",\"1\",\"39.2\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"Diabetes\"],[\"1\"],[\"1\"],[\"0\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Breast + cancer prediction model based on decision trees.\",\"id\":\"scikit-learn/cancer-prediction-trees\"}],\"spaces\":[{\"description\":\"An + application that can predict defective products on a production line.\",\"id\":\"scikit-learn/tabular-playground\"},{\"description\":\"An + application that compares various tabular classification techniques on different + datasets.\",\"id\":\"scikit-learn/classification\"}],\"summary\":\"Tabular + classification is the task of classifying a target category (a group) based + on set of attributes.\",\"widgetModels\":[\"scikit-learn/tabular-playground\"],\"youtubeId\":\"\",\"id\":\"tabular-classification\",\"label\":\"Tabular + Classification\",\"libraries\":[\"sklearn\"]},\"tabular-regression\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Car + Name\",\"Horsepower\",\"Weight\"],[\"ford torino\",\"140\",\"3,449\"],[\"amc + hornet\",\"97\",\"2,774\"],[\"toyota corolla\",\"65\",\"1,773\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"MPG + (miles per gallon)\"],[\"17\"],[\"18\"],[\"31\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"mse\"},{\"description\":\"Coefficient + of determination (or R-squared) is a measure of how well the model fits the + data. Higher R-squared is considered a better fit.\",\"id\":\"r-squared\"}],\"models\":[{\"description\":\"Fish + weight prediction based on length measurements and species.\",\"id\":\"scikit-learn/Fish-Weight\"}],\"spaces\":[{\"description\":\"An + application that can predict weight of a fish based on set of attributes.\",\"id\":\"scikit-learn/fish-weight-prediction\"}],\"summary\":\"Tabular + regression is the task of predicting a numerical value given a set of attributes.\",\"widgetModels\":[\"scikit-learn/Fish-Weight\"],\"youtubeId\":\"\",\"id\":\"tabular-regression\",\"label\":\"Tabular + Regression\",\"libraries\":[\"sklearn\"]},\"text-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"A + text classification dataset used to benchmark natural language inference models\",\"id\":\"stanfordnlp/snli\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love Hugging Face!\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"POSITIVE\",\"score\":0.9},{\"label\":\"NEUTRAL\",\"score\":0.1},{\"label\":\"NEGATIVE\",\"score\":0}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"The + F1 metric is the harmonic mean of the precision and recall. It can be calculated + as: F1 = 2 * (precision * recall) / (precision + recall)\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust model trained for sentiment analysis.\",\"id\":\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"},{\"description\":\"A + sentiment analysis model specialized in financial sentiment.\",\"id\":\"ProsusAI/finbert\"},{\"description\":\"A + sentiment analysis model specialized in analyzing tweets.\",\"id\":\"cardiffnlp/twitter-roberta-base-sentiment-latest\"},{\"description\":\"A + model that can classify languages.\",\"id\":\"papluca/xlm-roberta-base-language-detection\"},{\"description\":\"A + model that can classify text generation attacks.\",\"id\":\"meta-llama/Prompt-Guard-86M\"}],\"spaces\":[{\"description\":\"An + application that can classify financial sentiment.\",\"id\":\"IoannisTr/Tech_Stocks_Trading_Assistant\"},{\"description\":\"A + dashboard that contains various text classification tasks.\",\"id\":\"miesnerjacob/Multi-task-NLP\"},{\"description\":\"An + application that analyzes user reviews in healthcare.\",\"id\":\"spacy/healthsea-demo\"}],\"summary\":\"Text + Classification is the task of assigning a label or class to a given text. + Some use cases are sentiment analysis, natural language inference, and assessing + grammatical correctness.\",\"widgetModels\":[\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"],\"youtubeId\":\"leNG9fN9FQU\",\"id\":\"text-classification\",\"label\":\"Text + Classification\",\"libraries\":[\"adapter-transformers\",\"setfit\",\"spacy\",\"transformers\",\"transformers.js\"]},\"text-generation\":{\"datasets\":[{\"description\":\"A + large multilingual dataset of text crawled from the web.\",\"id\":\"mc4\"},{\"description\":\"Diverse + open-source data consisting of 22 smaller high-quality datasets. It was used + to train GPT-Neo.\",\"id\":\"the_pile\"},{\"description\":\"Truly open-source, + curated and cleaned dialogue dataset.\",\"id\":\"HuggingFaceH4/ultrachat_200k\"},{\"description\":\"An + instruction dataset with preference ratings on responses.\",\"id\":\"openbmb/UltraFeedback\"},{\"description\":\"A + large synthetic dataset for alignment of text generation models.\",\"id\":\"argilla/magpie-ultra-v0.1\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Once + upon a time,\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Once + upon a time, we knew that our ancestors were on the verge of extinction. The + great explorers and poets of the Old World, from Alexander the Great to Chaucer, + are dead and gone. A good many of our ancient explorers and poets have\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Cross + Entropy is a metric that calculates the difference between two probability + distributions. Each probability distribution is the distribution of predicted + words\",\"id\":\"Cross Entropy\"},{\"description\":\"The Perplexity metric + is the exponential of the cross-entropy loss. It evaluates the probabilities + assigned to the next word by the model. Lower perplexity indicates better + performance\",\"id\":\"Perplexity\"}],\"models\":[{\"description\":\"A text-generation + model trained to follow instructions.\",\"id\":\"google/gemma-2-2b-it\"},{\"description\":\"Very + powerful text generation model trained to follow instructions.\",\"id\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\"},{\"description\":\"Small + yet powerful text generation model.\",\"id\":\"microsoft/Phi-3-mini-4k-instruct\"},{\"description\":\"A + very powerful model that can solve mathematical problems.\",\"id\":\"AI-MO/NuminaMath-7B-TIR\"},{\"description\":\"Strong + text generation model to follow instructions.\",\"id\":\"Qwen/Qwen2.5-7B-Instruct\"},{\"description\":\"Very + strong open-source large language model.\",\"id\":\"nvidia/Llama-3.1-Nemotron-70B-Instruct\"}],\"spaces\":[{\"description\":\"A + leaderboard to compare different open-source text generation models based + on various benchmarks.\",\"id\":\"open-llm-leaderboard/open_llm_leaderboard\"},{\"description\":\"A + leaderboard for comparing chain-of-thought performance of models.\",\"id\":\"logikon/open_cot_leaderboard\"},{\"description\":\"An + text generation based application based on a very powerful LLaMA2 model.\",\"id\":\"ysharma/Explore_llamav2_with_TGI\"},{\"description\":\"An + text generation based application to converse with Zephyr model.\",\"id\":\"HuggingFaceH4/zephyr-chat\"},{\"description\":\"A + leaderboard that ranks text generation models based on blind votes from people.\",\"id\":\"lmsys/chatbot-arena-leaderboard\"},{\"description\":\"An + chatbot to converse with a very powerful text generation model.\",\"id\":\"mlabonne/phixtral-chat\"}],\"summary\":\"Generating + text is the task of generating new text given another text. These models can, + for example, fill in incomplete text or paraphrase.\",\"widgetModels\":[\"mistralai/Mistral-Nemo-Instruct-2407\"],\"youtubeId\":\"e9gNEAlsOvU\",\"id\":\"text-generation\",\"label\":\"Text + Generation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-image\":{\"datasets\":[{\"description\":\"RedCaps + is a large-scale dataset of 12M image-text pairs collected from Reddit.\",\"id\":\"red_caps\"},{\"description\":\"Conceptual + Captions is a dataset consisting of ~3.3M images annotated with captions.\",\"id\":\"conceptual_captions\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"A + city above clouds, pastel colors, Victorian style\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"image.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Inception Score (IS) measure assesses diversity and meaningfulness. It uses + a generated image sample to predict its label. A higher score signifies more + diverse and meaningful images.\",\"id\":\"IS\"},{\"description\":\"The Fr\xE9chet + Inception Distance (FID) calculates the distance between distributions between + synthetic and real samples. A lower FID score indicates better similarity + between the distributions of real and generated images.\",\"id\":\"FID\"},{\"description\":\"R-precision + assesses how the generated image aligns with the provided text description. + It uses the generated images as queries to retrieve relevant text descriptions. + The top 'r' relevant descriptions are selected and used to calculate R-precision + as r/R, where 'R' is the number of ground truth descriptions associated with + the generated images. A higher R-precision value indicates a better model.\",\"id\":\"R-Precision\"}],\"models\":[{\"description\":\"One + of the most powerful image generation models that can generate realistic outputs.\",\"id\":\"black-forest-labs/FLUX.1-dev\"},{\"description\":\"A + powerful yet fast image generation model.\",\"id\":\"latent-consistency/lcm-lora-sdxl\"},{\"description\":\"Text-to-image + model for photorealistic generation.\",\"id\":\"Kwai-Kolors/Kolors\"},{\"description\":\"A + powerful text-to-image model.\",\"id\":\"stabilityai/stable-diffusion-3-medium-diffusers\"}],\"spaces\":[{\"description\":\"A + powerful text-to-image application.\",\"id\":\"stabilityai/stable-diffusion-3-medium\"},{\"description\":\"A + text-to-image application to generate comics.\",\"id\":\"jbilcke-hf/ai-comic-factory\"},{\"description\":\"An + application to match multiple custom image generation models.\",\"id\":\"multimodalart/flux-lora-lab\"},{\"description\":\"A + powerful yet very fast image generation application.\",\"id\":\"latent-consistency/lcm-lora-for-sdxl\"},{\"description\":\"A + gallery to explore various text-to-image models.\",\"id\":\"multimodalart/LoraTheExplorer\"},{\"description\":\"An + application for `text-to-image`, `image-to-image` and image inpainting.\",\"id\":\"ArtGAN/Stable-Diffusion-ControlNet-WebUI\"},{\"description\":\"An + application to generate realistic images given photos of a person and a prompt.\",\"id\":\"InstantX/InstantID\"}],\"summary\":\"Text-to-image + is the task of generating images from input text. These pipelines can also + be used to modify and edit images based on text prompts.\",\"widgetModels\":[\"black-forest-labs/FLUX.1-dev\"],\"youtubeId\":\"\",\"id\":\"text-to-image\",\"label\":\"Text-to-Image\",\"libraries\":[\"diffusers\"]},\"text-to-speech\":{\"canonicalId\":\"text-to-audio\",\"datasets\":[{\"description\":\"10K + hours of multi-speaker English dataset.\",\"id\":\"parler-tts/mls_eng_10k\"},{\"description\":\"Multi-speaker + English dataset.\",\"id\":\"mythicinfinity/libritts_r\"},{\"description\":\"Mulit-lingual + dataset.\",\"id\":\"facebook/multilingual_librispeech\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love audio models on the Hub!\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"audio.wav\",\"type\":\"audio\"}]},\"metrics\":[{\"description\":\"The + Mel Cepstral Distortion (MCD) metric is used to calculate the quality of generated + speech.\",\"id\":\"mel cepstral distortion\"}],\"models\":[{\"description\":\"A + prompt based, powerful TTS model.\",\"id\":\"parler-tts/parler-tts-large-v1\"},{\"description\":\"A + powerful TTS model that supports English and Chinese.\",\"id\":\"SWivid/F5-TTS\"},{\"description\":\"A + massively multi-lingual TTS model.\",\"id\":\"coqui/XTTS-v2\"},{\"description\":\"A + powerful TTS model.\",\"id\":\"amphion/MaskGCT\"},{\"description\":\"A Llama + based TTS model.\",\"id\":\"OuteAI/OuteTTS-0.1-350M\"}],\"spaces\":[{\"description\":\"An + application for generate highly realistic, multilingual speech.\",\"id\":\"suno/bark\"},{\"description\":\"An + application on XTTS, a voice generation model that lets you clone voices into + different languages.\",\"id\":\"coqui/xtts\"},{\"description\":\"An application + that generates speech in different styles in English and Chinese.\",\"id\":\"mrfakename/E2-F5-TTS\"},{\"description\":\"An + application that synthesizes emotional speech for diverse speaker prompts.\",\"id\":\"parler-tts/parler-tts-expresso\"}],\"summary\":\"Text-to-Speech + (TTS) is the task of generating natural sounding speech given text input. + TTS models can be extended to have a single model that generates speech for + multiple speakers and multiple languages.\",\"widgetModels\":[\"suno/bark\"],\"youtubeId\":\"NW62DpzJ274\",\"id\":\"text-to-speech\",\"label\":\"Text-to-Speech\",\"libraries\":[\"espnet\",\"tensorflowtts\",\"transformers\",\"transformers.js\"]},\"text-to-video\":{\"datasets\":[{\"description\":\"Microsoft + Research Video to Text is a large-scale dataset for open domain video captioning\",\"id\":\"iejMac/CLIP-MSR-VTT\"},{\"description\":\"UCF101 + Human Actions dataset consists of 13,320 video clips from YouTube, with 101 + classes.\",\"id\":\"quchenyuan/UCF101-ZIP\"},{\"description\":\"A high-quality + dataset for human action recognition in YouTube videos.\",\"id\":\"nateraw/kinetics\"},{\"description\":\"A + dataset of video clips of humans performing pre-defined basic actions with + everyday objects.\",\"id\":\"HuggingFaceM4/something_something_v2\"},{\"description\":\"This + dataset consists of text-video pairs and contains noisy samples with irrelevant + video descriptions\",\"id\":\"HuggingFaceM4/webvid\"},{\"description\":\"A + dataset of short Flickr videos for the temporal localization of events with + descriptions.\",\"id\":\"iejMac/CLIP-DiDeMo\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Darth + Vader is surfing on the waves.\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"text-to-video-output.gif\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"Inception + Score uses an image classification model that predicts class labels and evaluates + how distinct and diverse the images are. A higher score indicates better video + generation.\",\"id\":\"is\"},{\"description\":\"Frechet Inception Distance + uses an image classification model to obtain image embeddings. The metric + compares mean and standard deviation of the embeddings of real and generated + images. A smaller score indicates better video generation.\",\"id\":\"fid\"},{\"description\":\"Frechet + Video Distance uses a model that captures coherence for changes in frames + and the quality of each frame. A smaller score indicates better video generation.\",\"id\":\"fvd\"},{\"description\":\"CLIPSIM + measures similarity between video frames and text using an image-text similarity + model. A higher score indicates better video generation.\",\"id\":\"clipsim\"}],\"models\":[{\"description\":\"A + strong model for consistent video generation.\",\"id\":\"rain1011/pyramid-flow-sd3\"},{\"description\":\"A + robust model for text-to-video generation.\",\"id\":\"VideoCrafter/VideoCrafter2\"},{\"description\":\"A + cutting-edge text-to-video generation model.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"}],\"spaces\":[{\"description\":\"An + application that generates video from text.\",\"id\":\"VideoCrafter/VideoCrafter\"},{\"description\":\"Consistent + video generation application.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"},{\"description\":\"A + cutting edge video generation application.\",\"id\":\"Pyramid-Flow/pyramid-flow\"}],\"summary\":\"Text-to-video + models can be used in any application that requires generating consistent + sequence of images from text. \",\"widgetModels\":[],\"id\":\"text-to-video\",\"label\":\"Text-to-Video\",\"libraries\":[\"diffusers\"]},\"token-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset useful to benchmark named entity recognition models.\",\"id\":\"eriktks/conll2003\"},{\"description\":\"A + multilingual dataset of Wikipedia articles annotated for named entity recognition + in over 150 different languages.\",\"id\":\"unimelb-nlp/wikiann\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"text\":\"My + name is Omar and I live in Z\xFCrich.\",\"tokens\":[{\"type\":\"PERSON\",\"start\":11,\"end\":15},{\"type\":\"GPE\",\"start\":30,\"end\":36}],\"type\":\"text-with-tokens\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust performance model to identify people, locations, organizations and + names of miscellaneous entities.\",\"id\":\"dslim/bert-base-NER\"},{\"description\":\"A + strong model to identify people, locations, organizations and names in multiple + languages.\",\"id\":\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"},{\"description\":\"A + token classification model specialized on medical entity recognition.\",\"id\":\"blaze999/Medical-NER\"},{\"description\":\"Flair + models are typically the state of the art in named entity recognition tasks.\",\"id\":\"flair/ner-english\"}],\"spaces\":[{\"description\":\"An + application that can recognizes entities, extracts noun chunks and recognizes + various linguistic features of each token.\",\"id\":\"spacy/gradio_pipeline_visualizer\"}],\"summary\":\"Token + classification is a natural language understanding task in which a label is + assigned to some tokens in a text. Some popular token classification subtasks + are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models + could be trained to identify specific entities in a text, such as dates, individuals + and places; and PoS tagging would identify, for example, which words in a + text are verbs, nouns, and punctuation marks.\",\"widgetModels\":[\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"],\"youtubeId\":\"wVHdVlPScxA\",\"id\":\"token-classification\",\"label\":\"Token + Classification\",\"libraries\":[\"adapter-transformers\",\"flair\",\"spacy\",\"span-marker\",\"stanza\",\"transformers\",\"transformers.js\"]},\"translation\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"A + dataset of copyright-free books translated into 16 different languages.\",\"id\":\"Helsinki-NLP/opus_books\"},{\"description\":\"An + example of translation between programming languages. This dataset consists + of functions in Java and C#.\",\"id\":\"google/code_x_glue_cc_code_to_code_trans\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Mein + Name ist Omar und ich wohne in Z\xFCrich.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"BLEU + score is calculated by counting the number of shared single or subsequent + tokens between the generated sequence and the reference. Subsequent n tokens + are called \u201Cn-grams\u201D. Unigram refers to a single token while bi-gram + refers to token pairs and n-grams refer to n subsequent tokens. The score + ranges from 0 to 1, where 1 means the translation perfectly matched and 0 + did not match at all\",\"id\":\"bleu\"},{\"description\":\"\",\"id\":\"sacrebleu\"}],\"models\":[{\"description\":\"Very + powerful model that can translate many languages between each other, especially + low-resource languages.\",\"id\":\"facebook/nllb-200-1.3B\"},{\"description\":\"A + general-purpose Transformer that can be used to translate from English to + German, French, or Romanian.\",\"id\":\"google-t5/t5-base\"}],\"spaces\":[{\"description\":\"An + application that can translate between 100 languages.\",\"id\":\"Iker/Translate-100-languages\"},{\"description\":\"An + application that can translate between many languages.\",\"id\":\"Geonmo/nllb-translation-demo\"}],\"summary\":\"Translation + is the task of converting text from one language to another.\",\"widgetModels\":[\"facebook/mbart-large-50-many-to-many-mmt\"],\"youtubeId\":\"1JvfrvZgi6c\",\"id\":\"translation\",\"label\":\"Translation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"unconditional-image-generation\":{\"datasets\":[{\"description\":\"The + CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with + 600 images per class.\",\"id\":\"cifar100\"},{\"description\":\"Multiple images + of celebrities, used for facial expression translation.\",\"id\":\"CelebA\"}],\"demo\":{\"inputs\":[{\"label\":\"Seed\",\"content\":\"42\",\"type\":\"text\"},{\"label\":\"Number + of images to generate:\",\"content\":\"4\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"unconditional-image-generation-output.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + inception score (IS) evaluates the quality of generated images. It measures + the diversity of the generated images (the model predictions are evenly distributed + across all possible labels) and their 'distinction' or 'sharpness' (the model + confidently predicts a single label for each image).\",\"id\":\"Inception + score (IS)\"},{\"description\":\"The Fr\xE9chet Inception Distance (FID) evaluates + the quality of images created by a generative model by calculating the distance + between feature vectors for real and generated images.\",\"id\":\"Fre\u0107het + Inception Distance (FID)\"}],\"models\":[{\"description\":\"High-quality image + generation model trained on the CIFAR-10 dataset. It synthesizes images of + the ten classes presented in the dataset using diffusion probabilistic models, + a class of latent variable models inspired by considerations from nonequilibrium + thermodynamics.\",\"id\":\"google/ddpm-cifar10-32\"},{\"description\":\"High-quality + image generation model trained on the 256x256 CelebA-HQ dataset. It synthesizes + images of faces using diffusion probabilistic models, a class of latent variable + models inspired by considerations from nonequilibrium thermodynamics.\",\"id\":\"google/ddpm-celebahq-256\"}],\"spaces\":[{\"description\":\"An + application that can generate realistic faces.\",\"id\":\"CompVis/celeba-latent-diffusion\"}],\"summary\":\"Unconditional + image generation is the task of generating images with no condition in any + context (like a prompt text or another image). Once trained, the model will + create images that resemble its training data distribution.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"unconditional-image-generation\",\"label\":\"Unconditional + Image Generation\",\"libraries\":[\"diffusers\"]},\"video-text-to-text\":{\"datasets\":[{\"description\":\"Multiple-choice + questions and answers about videos.\",\"id\":\"lmms-lab/Video-MME\"},{\"description\":\"A + dataset of instructions and question-answer pairs about videos.\",\"id\":\"lmms-lab/VideoChatGPT\"},{\"description\":\"Large + video understanding dataset.\",\"id\":\"HuggingFaceFV/finevideo\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-text-to-text-input.gif\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"What is happening in this video?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + video shows a series of images showing a fountain with water jets and a variety + of colorful flowers and butterflies in the background.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/llava-onevision-qwen2-72b-ov-hf\"},{\"description\":\"Large + and powerful video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/LLaVA-NeXT-Video-34B-hf\"}],\"spaces\":[{\"description\":\"An + application to chat with a video-text-to-text model.\",\"id\":\"llava-hf/video-llava\"},{\"description\":\"A + leaderboard for various video-text-to-text models.\",\"id\":\"opencompass/openvlm_video_leaderboard\"}],\"summary\":\"Video-text-to-text + models take in a video and a text prompt and output text. These models are + also called video-language models.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"video-text-to-text\",\"label\":\"Video-Text-to-Text\",\"libraries\":[\"transformers\"]},\"visual-question-answering\":{\"datasets\":[{\"description\":\"A + widely used dataset containing questions (with answers) about images.\",\"id\":\"Graphcore/vqa\"},{\"description\":\"A + dataset to benchmark visual reasoning based on text in images.\",\"id\":\"facebook/textvqa\"}],\"demo\":{\"inputs\":[{\"filename\":\"elephant.jpeg\",\"type\":\"img\"},{\"label\":\"Question\",\"content\":\"What + is in this image?\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"elephant\",\"score\":0.97},{\"label\":\"elephants\",\"score\":0.06},{\"label\":\"animal\",\"score\":0.003}]}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"Measures + how much a predicted answer differs from the ground truth based on the difference + in their semantic meaning.\",\"id\":\"wu-palmer similarity\"}],\"models\":[{\"description\":\"A + visual question answering model trained to convert charts and plots to text.\",\"id\":\"google/deplot\"},{\"description\":\"A + visual question answering model trained for mathematical reasoning and chart + derendering from images.\",\"id\":\"google/matcha-base\"},{\"description\":\"A + strong visual question answering that answers questions from book covers.\",\"id\":\"google/pix2struct-ocrvqa-large\"}],\"spaces\":[{\"description\":\"An + application that compares visual question answering models across different + tasks.\",\"id\":\"merve/pix2struct\"},{\"description\":\"An application that + can answer questions based on images.\",\"id\":\"nielsr/vilt-vqa\"},{\"description\":\"An + application that can caption images and answer questions about a given image. + \",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An application that can + caption images and answer questions about a given image. \",\"id\":\"vumichien/Img2Prompt\"}],\"summary\":\"Visual + Question Answering is the task of answering open-ended questions based on + an image. They output natural language responses to natural language questions.\",\"widgetModels\":[\"dandelin/vilt-b32-finetuned-vqa\"],\"youtubeId\":\"\",\"id\":\"visual-question-answering\",\"label\":\"Visual + Question Answering\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"The + Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced + collection of 433k sentence pairs annotated with textual entailment information.\",\"id\":\"nyu-mll/multi_nli\"},{\"description\":\"FEVER + is a publicly available dataset for fact extraction and verification against + textual sources.\",\"id\":\"fever/fever\"}],\"demo\":{\"inputs\":[{\"label\":\"Text + Input\",\"content\":\"Dune is the best movie ever.\",\"type\":\"text\"},{\"label\":\"Candidate + Labels\",\"content\":\"CINEMA, ART, MUSIC\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"CINEMA\",\"score\":0.9},{\"label\":\"ART\",\"score\":0.1},{\"label\":\"MUSIC\",\"score\":0}]}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + zero-shot text classification model.\",\"id\":\"facebook/bart-large-mnli\"},{\"description\":\"Powerful + zero-shot multilingual text classification model that can accomplish multiple + tasks.\",\"id\":\"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7\"}],\"spaces\":[],\"summary\":\"Zero-shot + text classification is a task in natural language processing where a model + is trained on a set of labeled examples but is then able to classify new examples + from previously unseen classes.\",\"widgetModels\":[\"facebook/bart-large-mnli\"],\"id\":\"zero-shot-classification\",\"label\":\"Zero-Shot + Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-image-classification\":{\"datasets\":[{\"description\":\"\",\"id\":\"\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-classification-input.jpeg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Cat\",\"score\":0.664},{\"label\":\"Dog\",\"score\":0.329},{\"label\":\"Bird\",\"score\":0.008}]}]},\"metrics\":[{\"description\":\"Computes + the number of times the correct label appears in top K labels predicted\",\"id\":\"top-K + accuracy\"}],\"models\":[{\"description\":\"Robust image classification model + trained on publicly available image-caption data.\",\"id\":\"openai/clip-vit-base-patch16\"},{\"description\":\"Strong + zero-shot image classification model.\",\"id\":\"google/siglip-so400m-patch14-224\"},{\"description\":\"Small + yet powerful zero-shot image classification model that can run on edge devices.\",\"id\":\"apple/MobileCLIP-S1-OpenCLIP\"},{\"description\":\"Strong + image classification model for biomedical domain.\",\"id\":\"microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224\"}],\"spaces\":[{\"description\":\"An + application that leverages zero-shot image classification to find best captions + to generate an image. \",\"id\":\"pharma/CLIP-Interrogator\"},{\"description\":\"An + application to compare different zero-shot image classification models. \",\"id\":\"merve/compare_clip_siglip\"}],\"summary\":\"Zero-shot + image classification is the task of classifying previously unseen classes + during training of a model.\",\"widgetModels\":[\"google/siglip-so400m-patch14-224\"],\"youtubeId\":\"\",\"id\":\"zero-shot-image-classification\",\"label\":\"Zero-Shot + Image Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-object-detection\":{\"datasets\":[],\"demo\":{\"inputs\":[{\"filename\":\"zero-shot-object-detection-input.jpg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"zero-shot-object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + zero-shot object detection model.\",\"id\":\"IDEA-Research/grounding-dino-base\"},{\"description\":\"Cutting-edge + zero-shot object detection model.\",\"id\":\"google/owlv2-base-patch16-ensemble\"}],\"spaces\":[{\"description\":\"A + demo to try the state-of-the-art zero-shot object detection model, OWLv2.\",\"id\":\"merve/owlv2\"},{\"description\":\"A + demo that combines a zero-shot object detection and mask generation model + for zero-shot segmentation.\",\"id\":\"merve/OWLSAM\"}],\"summary\":\"Zero-shot + object detection is a computer vision task to detect objects and their classes + in images, without any prior training or knowledge of the classes. Zero-shot + object detection models receive an image as input, as well as a list of candidate + classes, and output the bounding boxes and labels where the objects have been + detected.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"zero-shot-object-detection\",\"label\":\"Zero-Shot + Object Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"Descriptive + captions for 3D objects in Objaverse.\",\"id\":\"tiange/Cap3D\"}],\"demo\":{\"inputs\":[{\"label\":\"Prompt\",\"content\":\"a + cat statue\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"text-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Text-to-3D + mesh model by OpenAI\",\"id\":\"openai/shap-e\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Text-to-3D + demo with mesh outputs.\",\"id\":\"hysts/Shap-E\"},{\"description\":\"Text/image-to-3D + demo with splat outputs.\",\"id\":\"ashawkey/LGM\"}],\"summary\":\"Text-to-3D + models take in text input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"text-to-3d\",\"label\":\"Text-to-3D\",\"libraries\":[\"diffusers\"]},\"image-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"A + dataset of isolated object images for evaluating image-to-3D models.\",\"id\":\"dylanebert/iso3d\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-3d-image-input.png\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"image-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Fast + image-to-3D mesh model by Tencent.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Fast + image-to-3D mesh model by StabilityAI\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"A + scaled up image-to-3D mesh model derived from TripoSR.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate image-to-3D models.\",\"id\":\"dylanebert/3d-arena\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Image-to-3D + demo with splat outputs.\",\"id\":\"dylanebert/LGM-mini\"}],\"summary\":\"Image-to-3D + models take in image input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"image-to-3d\",\"label\":\"Image-to-3D\",\"libraries\":[\"diffusers\"]}}" + headers: + Access-Control-Allow-Origin: + - https://huggingface.co + Access-Control-Expose-Headers: + - X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range,X-Xet-Access-Token,X-Xet-Token-Expiration,X-Xet-Refresh-Route,X-Xet-Cas-Url,X-Xet-Hash + Connection: + - keep-alive + Content-Length: + - '74829' + Content-Type: + - application/json; charset=utf-8 + Date: + - Fri, 10 Jan 2025 20:22:28 GMT + ETag: + - W/"1244d-rY02FKAQRZwcoD2xI/AqH93OPBg" + Referrer-Policy: + - strict-origin-when-cross-origin + Vary: + - Origin + Via: + - 1.1 2fe3b10bc3da6ef618e3ab1598c2020c.cloudfront.net (CloudFront) + X-Amz-Cf-Id: + - xb8qOCqrsziWPQgEoy7Fd0G5yLmhLG9krMLlDUT7IWCebNtTn4k5Sw== + X-Amz-Cf-Pop: + - BOG50-C1 + X-Cache: + - Miss from cloudfront + X-Powered-By: + - huggingface-moon + X-Request-Id: + - 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towel","score":0.1261865645647049},{"label":"carton","score":0.05506785213947296}]' + headers: + Access-Control-Allow-Credentials: + - 'true' + Access-Control-Expose-Headers: + - x-compute-type, x-compute-time + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 10 Jan 2025 20:22:39 GMT + Server: + - uvicorn + Transfer-Encoding: + - chunked + Vary: + - Origin, Access-Control-Request-Method, Access-Control-Request-Headers + x-compute-characters: + - '-1' + x-compute-time: + - '0.226' + x-compute-type: + - cpu + x-request-id: + - pYM7a4J8lZVAs5FDTDgMO + x-sha: + - 3f49326eb077187dfe1c2a2bb15fbd74e6ab91e3 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_huggingface_hub_image_segmentation.yaml b/tests/cassettes/test_huggingface_hub_image_segmentation.yaml new file mode 100644 index 0000000..c956860 --- /dev/null +++ b/tests/cassettes/test_huggingface_hub_image_segmentation.yaml @@ -0,0 +1,29523 @@ +interactions: +- request: + 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It is calculated for + each semantic class separately\",\"id\":\"Average Precision\"},{\"description\":\"Mean + Average Precision (mAP) is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"Intersection over Union (IoU) is the + overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic + classes\",\"id\":\"Mean Intersection over Union\"},{\"description\":\"AP\u03B1 + is the Average Precision at the IoU threshold of a \u03B1 value, for example, + AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + semantic segmentation model trained on ADE20k.\",\"id\":\"openmmlab/upernet-convnext-small\"},{\"description\":\"Background + removal model.\",\"id\":\"briaai/RMBG-1.4\"},{\"description\":\"A multipurpose + image segmentation model for high resolution images.\",\"id\":\"ZhengPeng7/BiRefNet\"},{\"description\":\"Powerful + human-centric image segmentation model.\",\"id\":\"facebook/sapiens-seg-1b\"},{\"description\":\"Panoptic + segmentation model trained on the COCO (common objects) dataset.\",\"id\":\"facebook/mask2former-swin-large-coco-panoptic\"}],\"spaces\":[{\"description\":\"A + semantic segmentation application that can predict unseen instances out of + the box.\",\"id\":\"facebook/ov-seg\"},{\"description\":\"One of the strongest + segmentation applications.\",\"id\":\"jbrinkma/segment-anything\"},{\"description\":\"A + human-centric segmentation model.\",\"id\":\"facebook/sapiens-pose\"},{\"description\":\"An + instance segmentation application to predict neuronal cell types from microscopy + images.\",\"id\":\"rashmi/sartorius-cell-instance-segmentation\"},{\"description\":\"An + application that segments videos.\",\"id\":\"ArtGAN/Segment-Anything-Video\"},{\"description\":\"An + panoptic segmentation application built for outdoor environments.\",\"id\":\"segments/panoptic-segment-anything\"}],\"summary\":\"Image + Segmentation divides an image into segments where each pixel in the image + is mapped to an object. This task has multiple variants such as instance segmentation, + panoptic segmentation and semantic segmentation.\",\"widgetModels\":[\"nvidia/segformer-b0-finetuned-ade-512-512\"],\"youtubeId\":\"dKE8SIt9C-w\",\"id\":\"image-segmentation\",\"label\":\"Image + Segmentation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"image-to-image\":{\"datasets\":[{\"description\":\"Synthetic + dataset, for image relighting\",\"id\":\"VIDIT\"},{\"description\":\"Multiple + images of celebrities, used for facial expression translation\",\"id\":\"huggan/CelebA-faces\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-image-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"image-to-image-output.png\",\"type\":\"img\"}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"Peak + Signal to Noise Ratio (PSNR) is an approximation of the human perception, + considering the ratio of the absolute intensity with respect to the variations. + Measured in dB, a high value indicates a high fidelity.\",\"id\":\"PSNR\"},{\"description\":\"Structural + Similarity Index (SSIM) is a perceptual metric which compares the luminance, + contrast and structure of two images. The values of SSIM range between -1 + and 1, and higher values indicate closer resemblance to the original image.\",\"id\":\"SSIM\"},{\"description\":\"Inception + Score (IS) is an analysis of the labels predicted by an image classification + model when presented with a sample of the generated images.\",\"id\":\"IS\"}],\"models\":[{\"description\":\"An + image-to-image model to improve image resolution.\",\"id\":\"fal/AuraSR-v2\"},{\"description\":\"A + model that increases the resolution of an image.\",\"id\":\"keras-io/super-resolution\"},{\"description\":\"A + model that creates a set of variations of the input image in the style of + DALL-E using Stable Diffusion.\",\"id\":\"lambdalabs/sd-image-variations-diffusers\"},{\"description\":\"A + model that generates images based on segments in the input image and the text + prompt.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"A + model that takes an image and an instruction to edit the image.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"spaces\":[{\"description\":\"Image + enhancer application for low light.\",\"id\":\"keras-io/low-light-image-enhancement\"},{\"description\":\"Style + transfer application.\",\"id\":\"keras-io/neural-style-transfer\"},{\"description\":\"An + application that generates images based on segment control.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"Image + generation application that takes image control and text prompt.\",\"id\":\"hysts/ControlNet\"},{\"description\":\"Colorize + any image using this app.\",\"id\":\"ioclab/brightness-controlnet\"},{\"description\":\"Edit + images with instructions.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"summary\":\"Image-to-image + is the task of transforming an input image through a variety of possible manipulations + and enhancements, such as super-resolution, image inpainting, colorization, + and more.\",\"widgetModels\":[\"stabilityai/stable-diffusion-2-inpainting\"],\"youtubeId\":\"\",\"id\":\"image-to-image\",\"label\":\"Image-to-Image\",\"libraries\":[\"diffusers\",\"transformers\",\"transformers.js\"]},\"image-text-to-text\":{\"datasets\":[{\"description\":\"Instructions + composed of image and text.\",\"id\":\"liuhaotian/LLaVA-Instruct-150K\"},{\"description\":\"Conversation + turns where questions involve image and text.\",\"id\":\"liuhaotian/LLaVA-Pretrain\"},{\"description\":\"A + collection of datasets made for model fine-tuning.\",\"id\":\"HuggingFaceM4/the_cauldron\"},{\"description\":\"Screenshots + of websites with their HTML/CSS codes.\",\"id\":\"HuggingFaceM4/WebSight\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-text-to-text-input.png\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"Describe the position of the bee in detail.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + bee is sitting on a pink flower, surrounded by other flowers. The bee is positioned + in the center of the flower, with its head and front legs sticking out.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + vision language model with great visual understanding and reasoning capabilities.\",\"id\":\"meta-llama/Llama-3.2-11B-Vision-Instruct\"},{\"description\":\"Cutting-edge + vision language models.\",\"id\":\"allenai/Molmo-7B-D-0924\"},{\"description\":\"Small + yet powerful model.\",\"id\":\"vikhyatk/moondream2\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"Qwen/Qwen2-VL-7B-Instruct\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"mistralai/Pixtral-12B-2409\"},{\"description\":\"Strong + image-text-to-text model focused on documents.\",\"id\":\"stepfun-ai/GOT-OCR2_0\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate vision language models.\",\"id\":\"opencompass/open_vlm_leaderboard\"},{\"description\":\"Vision + language models arena, where models are ranked by votes of users.\",\"id\":\"WildVision/vision-arena\"},{\"description\":\"Powerful + vision-language model assistant.\",\"id\":\"akhaliq/Molmo-7B-D-0924\"},{\"description\":\"An + image-text-to-text application focused on documents.\",\"id\":\"stepfun-ai/GOT_official_online_demo\"},{\"description\":\"An + application to compare outputs of different vision language models.\",\"id\":\"merve/compare_VLMs\"},{\"description\":\"An + application for chatting with an image-text-to-text model.\",\"id\":\"GanymedeNil/Qwen2-VL-7B\"}],\"summary\":\"Image-text-to-text + models take in an image and text prompt and output text. These models are + also called vision-language models, or VLMs. The difference from image-to-text + models is that these models take an additional text input, not restricting + the model to certain use cases like image captioning, and may also be trained + to accept a conversation as input.\",\"widgetModels\":[\"meta-llama/Llama-3.2-11B-Vision-Instruct\"],\"youtubeId\":\"IoGaGfU1CIg\",\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"libraries\":[\"transformers\"]},\"image-to-text\":{\"datasets\":[{\"description\":\"Dataset + from 12M image-text of Reddit\",\"id\":\"red_caps\"},{\"description\":\"Dataset + from 3.3M images of Google\",\"id\":\"datasets/conceptual_captions\"}],\"demo\":{\"inputs\":[{\"filename\":\"savanna.jpg\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Detailed + description\",\"content\":\"a herd of giraffes and zebras grazing in a field\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust image captioning model.\",\"id\":\"Salesforce/blip2-opt-2.7b\"},{\"description\":\"A + powerful and accurate image-to-text model that can also localize concepts + in images.\",\"id\":\"microsoft/kosmos-2-patch14-224\"},{\"description\":\"A + strong optical character recognition model.\",\"id\":\"facebook/nougat-base\"},{\"description\":\"A + powerful model that lets you have a conversation with the image.\",\"id\":\"llava-hf/llava-1.5-7b-hf\"}],\"spaces\":[{\"description\":\"An + application that compares various image captioning models.\",\"id\":\"nielsr/comparing-captioning-models\"},{\"description\":\"A + robust image captioning application.\",\"id\":\"flax-community/image-captioning\"},{\"description\":\"An + application that transcribes handwritings into text.\",\"id\":\"nielsr/TrOCR-handwritten\"},{\"description\":\"An + application that can caption images and answer questions about a given image.\",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An + application that can caption images and answer questions with a conversational + agent.\",\"id\":\"Salesforce/BLIP2\"},{\"description\":\"An image captioning + application that demonstrates the effect of noise on captions.\",\"id\":\"johko/capdec-image-captioning\"}],\"summary\":\"Image + to text models output a text from a given image. Image captioning or optical + character recognition can be considered as the most common applications of + image to text.\",\"widgetModels\":[\"Salesforce/blip-image-captioning-large\"],\"youtubeId\":\"\",\"id\":\"image-to-text\",\"label\":\"Image-to-Text\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"keypoint-detection\":{\"datasets\":[{\"description\":\"A + dataset of hand keypoints of over 500k examples.\",\"id\":\"Vincent-luo/hagrid-mediapipe-hands\"}],\"demo\":{\"inputs\":[{\"filename\":\"keypoint-detection-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"keypoint-detection-output.png\",\"type\":\"img\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust keypoint detection model.\",\"id\":\"magic-leap-community/superpoint\"},{\"description\":\"Strong + keypoint detection model used to detect human pose.\",\"id\":\"facebook/sapiens-pose-1b\"}],\"spaces\":[{\"description\":\"An + application that detects hand keypoints in real-time.\",\"id\":\"datasciencedojo/Hand-Keypoint-Detection-Realtime\"},{\"description\":\"An + application to try a universal keypoint detection model.\",\"id\":\"merve/SuperPoint\"}],\"summary\":\"Keypoint + detection is the task of identifying meaningful distinctive points or features + in an image.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"keypoint-detection\",\"label\":\"Keypoint + Detection\",\"libraries\":[\"transformers\"]},\"mask-generation\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple Vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Medical + Imaging dataset of the Human Brain for segmentation and mask generating tasks\",\"id\":\"rocky93/BraTS_segmentation\"}],\"demo\":{\"inputs\":[{\"filename\":\"mask-generation-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"mask-generation-output.png\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"IoU + is used to measure the overlap between predicted mask and the ground truth + mask.\",\"id\":\"Intersection over Union (IoU)\"}],\"models\":[{\"description\":\"Small + yet powerful mask generation model.\",\"id\":\"Zigeng/SlimSAM-uniform-50\"},{\"description\":\"Very + strong mask generation model.\",\"id\":\"facebook/sam2-hiera-large\"}],\"spaces\":[{\"description\":\"An + application that combines a mask generation model with a zero-shot object + detection model for text-guided image segmentation.\",\"id\":\"merve/OWLSAM2\"},{\"description\":\"An + application that compares the performance of a large and a small mask generation + model.\",\"id\":\"merve/slimsam\"},{\"description\":\"An application based + on an improved mask generation model.\",\"id\":\"SkalskiP/segment-anything-model-2\"},{\"description\":\"An + application to remove objects from videos using mask generation models.\",\"id\":\"SkalskiP/SAM_and_ProPainter\"}],\"summary\":\"Mask + generation is the task of generating masks that identify a specific object + or region of interest in a given image. Masks are often used in segmentation + tasks, where they provide a precise way to isolate the object of interest + for further processing or analysis.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"mask-generation\",\"label\":\"Mask + Generation\",\"libraries\":[\"transformers\"]},\"object-detection\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Multi-task + computer vision benchmark.\",\"id\":\"merve/pascal-voc\"}],\"demo\":{\"inputs\":[{\"filename\":\"object-detection-input.jpg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + object detection model pre-trained on the COCO 2017 dataset.\",\"id\":\"facebook/detr-resnet-50\"},{\"description\":\"Real-time + and accurate object detection model.\",\"id\":\"jameslahm/yolov10x\"},{\"description\":\"Fast + and accurate object detection model trained on COCO and Object365 datasets.\",\"id\":\"PekingU/rtdetr_r18vd_coco_o365\"}],\"spaces\":[{\"description\":\"Leaderboard + to compare various object detection models across several metrics.\",\"id\":\"hf-vision/object_detection_leaderboard\"},{\"description\":\"An + application that contains various object detection models to try from.\",\"id\":\"Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS\"},{\"description\":\"An + application that shows multiple cutting edge techniques for object detection + and tracking.\",\"id\":\"kadirnar/torchyolo\"},{\"description\":\"An object + tracking, segmentation and inpainting application.\",\"id\":\"VIPLab/Track-Anything\"},{\"description\":\"Very + fast object tracking application based on object detection.\",\"id\":\"merve/RT-DETR-tracking-coco\"}],\"summary\":\"Object + Detection models allow users to identify objects of certain defined classes. + Object detection models receive an image as input and output the images with + bounding boxes and labels on detected objects.\",\"widgetModels\":[\"facebook/detr-resnet-50\"],\"youtubeId\":\"WdAeKSOpxhw\",\"id\":\"object-detection\",\"label\":\"Object + Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"video-classification\":{\"datasets\":[{\"description\":\"Benchmark + dataset used for video classification with videos that belong to 400 classes.\",\"id\":\"kinetics400\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-classification-input.gif\",\"type\":\"img\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Playing + Guitar\",\"score\":0.514},{\"label\":\"Playing Tennis\",\"score\":0.193},{\"label\":\"Cooking\",\"score\":0.068}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"google/vivit-b-16x2-kinetics400\"},{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"microsoft/xclip-base-patch32\"}],\"spaces\":[{\"description\":\"An + application that classifies video at different timestamps.\",\"id\":\"nateraw/lavila\"},{\"description\":\"An + application that classifies video.\",\"id\":\"fcakyon/video-classification\"}],\"summary\":\"Video + classification is the task of assigning a label or class to an entire video. + Videos are expected to have only one class for each video. Video classification + models take a video as input and return a prediction about which class the + video belongs to.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"video-classification\",\"label\":\"Video + Classification\",\"libraries\":[\"transformers\"]},\"question-answering\":{\"datasets\":[{\"description\":\"A + famous question answering dataset based on English articles from Wikipedia.\",\"id\":\"squad_v2\"},{\"description\":\"A + dataset of aggregated anonymized actual queries issued to the Google search + engine.\",\"id\":\"natural_questions\"}],\"demo\":{\"inputs\":[{\"label\":\"Question\",\"content\":\"Which + name is also used to describe the Amazon rainforest in English?\",\"type\":\"text\"},{\"label\":\"Context\",\"content\":\"The + Amazon rainforest, also known in English as Amazonia or the Amazon Jungle\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"Amazonia\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Exact + Match is a metric based on the strict character match of the predicted answer + and the right answer. For answers predicted correctly, the Exact Match will + be 1. Even if only one character is different, Exact Match will be 0\",\"id\":\"exact-match\"},{\"description\":\" + The F1-Score metric is useful if we value both false positives and false negatives + equally. The F1-Score is calculated on each word in the predicted sequence + against the correct answer\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust baseline model for most question answering domains.\",\"id\":\"deepset/roberta-base-squad2\"},{\"description\":\"Small + yet robust model that can answer questions.\",\"id\":\"distilbert/distilbert-base-cased-distilled-squad\"},{\"description\":\"A + special model that can answer questions from tables.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that can answer a long question from Wikipedia.\",\"id\":\"deepset/wikipedia-assistant\"}],\"summary\":\"Question + Answering models can retrieve the answer to a question from a given text, + which is useful for searching for an answer in a document. Some question answering + models can generate answers without context!\",\"widgetModels\":[\"deepset/roberta-base-squad2\"],\"youtubeId\":\"ajPx5LwJD-I\",\"id\":\"question-answering\",\"label\":\"Question + Answering\",\"libraries\":[\"adapter-transformers\",\"allennlp\",\"transformers\",\"transformers.js\"]},\"reinforcement-learning\":{\"datasets\":[{\"description\":\"A + curation of widely used datasets for Data Driven Deep Reinforcement Learning + (D4RL)\",\"id\":\"edbeeching/decision_transformer_gym_replay\"}],\"demo\":{\"inputs\":[{\"label\":\"State\",\"content\":\"Red + traffic light, pedestrians are about to pass.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Action\",\"content\":\"Stop + the car.\",\"type\":\"text\"},{\"label\":\"Next State\",\"content\":\"Yellow + light, pedestrians have crossed.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Accumulated + reward across all time steps discounted by a factor that ranges between 0 + and 1 and determines how much the agent optimizes for future relative to immediate + rewards. Measures how good is the policy ultimately found by a given algorithm + considering uncertainty over the future.\",\"id\":\"Discounted Total Reward\"},{\"description\":\"Average + return obtained after running the policy for a certain number of evaluation + episodes. As opposed to total reward, mean reward considers how much reward + a given algorithm receives while learning.\",\"id\":\"Mean Reward\"},{\"description\":\"Measures + how good a given algorithm is after a predefined time. Some algorithms may + be guaranteed to converge to optimal behavior across many time steps. However, + an agent that reaches an acceptable level of optimality after a given time + horizon may be preferable to one that ultimately reaches optimality but takes + a long time.\",\"id\":\"Level of Performance After Some Time\"}],\"models\":[{\"description\":\"A + Reinforcement Learning model trained on expert data from the Gym Hopper environment\",\"id\":\"edbeeching/decision-transformer-gym-hopper-expert\"},{\"description\":\"A + PPO agent playing seals/CartPole-v0 using the stable-baselines3 library and + the RL Zoo.\",\"id\":\"HumanCompatibleAI/ppo-seals-CartPole-v0\"}],\"spaces\":[{\"description\":\"An + application for a cute puppy agent learning to catch a stick.\",\"id\":\"ThomasSimonini/Huggy\"},{\"description\":\"An + application to play Snowball Fight with a reinforcement learning agent.\",\"id\":\"ThomasSimonini/SnowballFight\"}],\"summary\":\"Reinforcement + learning is the computational approach of learning from action by interacting + with an environment through trial and error and receiving rewards (negative + or positive) as feedback\",\"widgetModels\":[],\"youtubeId\":\"q0BiUn5LiBc\",\"id\":\"reinforcement-learning\",\"label\":\"Reinforcement + Learning\",\"libraries\":[\"transformers\",\"stable-baselines3\",\"ml-agents\",\"sample-factory\"]},\"sentence-similarity\":{\"datasets\":[{\"description\":\"Bing + queries with relevant passages from various web sources.\",\"id\":\"ms_marco\"}],\"demo\":{\"inputs\":[{\"label\":\"Source + sentence\",\"content\":\"Machine learning is so easy.\",\"type\":\"text\"},{\"label\":\"Sentences + to compare to\",\"content\":\"Deep learning is so straightforward.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"This + is so difficult, like rocket science.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"I + can't believe how much I struggled with this.\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Deep + learning is so straightforward.\",\"score\":0.623},{\"label\":\"This is so + difficult, like rocket science.\",\"score\":0.413},{\"label\":\"I can't believe + how much I struggled with this.\",\"score\":0.256}]}]},\"metrics\":[{\"description\":\"Reciprocal + Rank is a measure used to rank the relevancy of documents given a set of documents. + Reciprocal Rank is the reciprocal of the rank of the document retrieved, meaning, + if the rank is 3, the Reciprocal Rank is 0.33. If the rank is 1, the Reciprocal + Rank is 1\",\"id\":\"Mean Reciprocal Rank\"},{\"description\":\"The similarity + of the embeddings is evaluated mainly on cosine similarity. It is calculated + as the cosine of the angle between two vectors. It is particularly useful + when your texts are not the same length\",\"id\":\"Cosine Similarity\"}],\"models\":[{\"description\":\"This + model works well for sentences and paragraphs and can be used for clustering/grouping + and semantic searches.\",\"id\":\"sentence-transformers/all-mpnet-base-v2\"},{\"description\":\"A + multilingual robust sentence similarity model..\",\"id\":\"BAAI/bge-m3\"}],\"spaces\":[{\"description\":\"An + application that leverages sentence similarity to answer questions from YouTube + videos.\",\"id\":\"Gradio-Blocks/Ask_Questions_To_YouTube_Videos\"},{\"description\":\"An + application that retrieves relevant PubMed abstracts for a given online article + which can be used as further references.\",\"id\":\"Gradio-Blocks/pubmed-abstract-retriever\"},{\"description\":\"An + application that leverages sentence similarity to summarize text.\",\"id\":\"nickmuchi/article-text-summarizer\"},{\"description\":\"A + guide that explains how Sentence Transformers can be used for semantic search.\",\"id\":\"sentence-transformers/Sentence_Transformers_for_semantic_search\"}],\"summary\":\"Sentence + Similarity is the task of determining how similar two texts are. Sentence + similarity models convert input texts into vectors (embeddings) that capture + semantic information and calculate how close (similar) they are between them. + This task is particularly useful for information retrieval and clustering/grouping.\",\"widgetModels\":[\"BAAI/bge-small-en-v1.5\"],\"youtubeId\":\"VCZq5AkbNEU\",\"id\":\"sentence-similarity\",\"label\":\"Sentence + Similarity\",\"libraries\":[\"sentence-transformers\",\"spacy\",\"transformers.js\"]},\"summarization\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"News + articles in five different languages along with their summaries. Widely used + for benchmarking multilingual summarization models.\",\"id\":\"mlsum\"},{\"description\":\"English + conversations and their summaries. Useful for benchmarking conversational + agents.\",\"id\":\"samsum\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building, and the tallest structure in Paris. Its base is square, measuring + 125 metres (410 ft) on each side. It was the first structure to reach a height + of 300 metres. Excluding transmitters, the Eiffel Tower is the second tallest + free-standing structure in France after the Millau Viaduct.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building. It was the first structure to reach a height of 300 metres.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"The + generated sequence is compared against its summary, and the overlap of tokens + are counted. ROUGE-N refers to overlap of N subsequent tokens, ROUGE-1 refers + to overlap of single tokens and ROUGE-2 is the overlap of two subsequent tokens.\",\"id\":\"rouge\"}],\"models\":[{\"description\":\"A + strong summarization model trained on English news articles. Excels at generating + factual summaries.\",\"id\":\"facebook/bart-large-cnn\"},{\"description\":\"A + summarization model trained on medical articles.\",\"id\":\"Falconsai/medical_summarization\"}],\"spaces\":[{\"description\":\"An + application that can summarize long paragraphs.\",\"id\":\"pszemraj/summarize-long-text\"},{\"description\":\"A + much needed summarization application for terms and conditions.\",\"id\":\"ml6team/distilbart-tos-summarizer-tosdr\"},{\"description\":\"An + application that summarizes long documents.\",\"id\":\"pszemraj/document-summarization\"},{\"description\":\"An + application that can detect errors in abstractive summarization.\",\"id\":\"ml6team/post-processing-summarization\"}],\"summary\":\"Summarization + is the task of producing a shorter version of a document while preserving + its important information. Some models can extract text from the original + input, while other models can generate entirely new text.\",\"widgetModels\":[\"facebook/bart-large-cnn\"],\"youtubeId\":\"yHnr5Dk2zCI\",\"id\":\"summarization\",\"label\":\"Summarization\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"table-question-answering\":{\"datasets\":[{\"description\":\"The + WikiTableQuestions dataset is a large-scale dataset for the task of question + answering on semi-structured tables.\",\"id\":\"wikitablequestions\"},{\"description\":\"WikiSQL + is a dataset of 80654 hand-annotated examples of questions and SQL queries + distributed across 24241 tables from Wikipedia.\",\"id\":\"wikisql\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Rank\",\"Name\",\"No.of + reigns\",\"Combined days\"],[\"1\",\"lou Thesz\",\"3\",\"3749\"],[\"2\",\"Ric + Flair\",\"8\",\"3103\"],[\"3\",\"Harley Race\",\"7\",\"1799\"]],\"type\":\"tabular\"},{\"label\":\"Question\",\"content\":\"What + is the number of reigns for Harley Race?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"7\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Checks + whether the predicted answer(s) is the same as the ground-truth answer(s).\",\"id\":\"Denotation + Accuracy\"}],\"models\":[{\"description\":\"A table question answering model + that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL + query on a given table.\",\"id\":\"microsoft/tapex-base\"},{\"description\":\"A + robust table question answering model.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that answers questions based on table CSV files.\",\"id\":\"katanaml/table-query\"}],\"summary\":\"Table + Question Answering (Table QA) is the answering a question about an information + on a given table.\",\"widgetModels\":[\"google/tapas-base-finetuned-wtq\"],\"id\":\"table-question-answering\",\"label\":\"Table + Question Answering\",\"libraries\":[\"transformers\"]},\"tabular-classification\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Glucose\",\"Blood + Pressure \",\"Skin Thickness\",\"Insulin\",\"BMI\"],[\"148\",\"72\",\"35\",\"0\",\"33.6\"],[\"150\",\"50\",\"30\",\"0\",\"35.1\"],[\"141\",\"60\",\"29\",\"1\",\"39.2\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"Diabetes\"],[\"1\"],[\"1\"],[\"0\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Breast + cancer prediction model based on decision trees.\",\"id\":\"scikit-learn/cancer-prediction-trees\"}],\"spaces\":[{\"description\":\"An + application that can predict defective products on a production line.\",\"id\":\"scikit-learn/tabular-playground\"},{\"description\":\"An + application that compares various tabular classification techniques on different + datasets.\",\"id\":\"scikit-learn/classification\"}],\"summary\":\"Tabular + classification is the task of classifying a target category (a group) based + on set of attributes.\",\"widgetModels\":[\"scikit-learn/tabular-playground\"],\"youtubeId\":\"\",\"id\":\"tabular-classification\",\"label\":\"Tabular + Classification\",\"libraries\":[\"sklearn\"]},\"tabular-regression\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Car + Name\",\"Horsepower\",\"Weight\"],[\"ford torino\",\"140\",\"3,449\"],[\"amc + hornet\",\"97\",\"2,774\"],[\"toyota corolla\",\"65\",\"1,773\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"MPG + (miles per gallon)\"],[\"17\"],[\"18\"],[\"31\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"mse\"},{\"description\":\"Coefficient + of determination (or R-squared) is a measure of how well the model fits the + data. Higher R-squared is considered a better fit.\",\"id\":\"r-squared\"}],\"models\":[{\"description\":\"Fish + weight prediction based on length measurements and species.\",\"id\":\"scikit-learn/Fish-Weight\"}],\"spaces\":[{\"description\":\"An + application that can predict weight of a fish based on set of attributes.\",\"id\":\"scikit-learn/fish-weight-prediction\"}],\"summary\":\"Tabular + regression is the task of predicting a numerical value given a set of attributes.\",\"widgetModels\":[\"scikit-learn/Fish-Weight\"],\"youtubeId\":\"\",\"id\":\"tabular-regression\",\"label\":\"Tabular + Regression\",\"libraries\":[\"sklearn\"]},\"text-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"A + text classification dataset used to benchmark natural language inference models\",\"id\":\"stanfordnlp/snli\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love Hugging Face!\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"POSITIVE\",\"score\":0.9},{\"label\":\"NEUTRAL\",\"score\":0.1},{\"label\":\"NEGATIVE\",\"score\":0}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"The + F1 metric is the harmonic mean of the precision and recall. It can be calculated + as: F1 = 2 * (precision * recall) / (precision + recall)\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust model trained for sentiment analysis.\",\"id\":\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"},{\"description\":\"A + sentiment analysis model specialized in financial sentiment.\",\"id\":\"ProsusAI/finbert\"},{\"description\":\"A + sentiment analysis model specialized in analyzing tweets.\",\"id\":\"cardiffnlp/twitter-roberta-base-sentiment-latest\"},{\"description\":\"A + model that can classify languages.\",\"id\":\"papluca/xlm-roberta-base-language-detection\"},{\"description\":\"A + model that can classify text generation attacks.\",\"id\":\"meta-llama/Prompt-Guard-86M\"}],\"spaces\":[{\"description\":\"An + application that can classify financial sentiment.\",\"id\":\"IoannisTr/Tech_Stocks_Trading_Assistant\"},{\"description\":\"A + dashboard that contains various text classification tasks.\",\"id\":\"miesnerjacob/Multi-task-NLP\"},{\"description\":\"An + application that analyzes user reviews in healthcare.\",\"id\":\"spacy/healthsea-demo\"}],\"summary\":\"Text + Classification is the task of assigning a label or class to a given text. + Some use cases are sentiment analysis, natural language inference, and assessing + grammatical correctness.\",\"widgetModels\":[\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"],\"youtubeId\":\"leNG9fN9FQU\",\"id\":\"text-classification\",\"label\":\"Text + Classification\",\"libraries\":[\"adapter-transformers\",\"setfit\",\"spacy\",\"transformers\",\"transformers.js\"]},\"text-generation\":{\"datasets\":[{\"description\":\"A + large multilingual dataset of text crawled from the web.\",\"id\":\"mc4\"},{\"description\":\"Diverse + open-source data consisting of 22 smaller high-quality datasets. It was used + to train GPT-Neo.\",\"id\":\"the_pile\"},{\"description\":\"Truly open-source, + curated and cleaned dialogue dataset.\",\"id\":\"HuggingFaceH4/ultrachat_200k\"},{\"description\":\"An + instruction dataset with preference ratings on responses.\",\"id\":\"openbmb/UltraFeedback\"},{\"description\":\"A + large synthetic dataset for alignment of text generation models.\",\"id\":\"argilla/magpie-ultra-v0.1\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Once + upon a time,\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Once + upon a time, we knew that our ancestors were on the verge of extinction. The + great explorers and poets of the Old World, from Alexander the Great to Chaucer, + are dead and gone. A good many of our ancient explorers and poets have\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Cross + Entropy is a metric that calculates the difference between two probability + distributions. Each probability distribution is the distribution of predicted + words\",\"id\":\"Cross Entropy\"},{\"description\":\"The Perplexity metric + is the exponential of the cross-entropy loss. It evaluates the probabilities + assigned to the next word by the model. Lower perplexity indicates better + performance\",\"id\":\"Perplexity\"}],\"models\":[{\"description\":\"A text-generation + model trained to follow instructions.\",\"id\":\"google/gemma-2-2b-it\"},{\"description\":\"Very + powerful text generation model trained to follow instructions.\",\"id\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\"},{\"description\":\"Small + yet powerful text generation model.\",\"id\":\"microsoft/Phi-3-mini-4k-instruct\"},{\"description\":\"A + very powerful model that can solve mathematical problems.\",\"id\":\"AI-MO/NuminaMath-7B-TIR\"},{\"description\":\"Strong + text generation model to follow instructions.\",\"id\":\"Qwen/Qwen2.5-7B-Instruct\"},{\"description\":\"Very + strong open-source large language model.\",\"id\":\"nvidia/Llama-3.1-Nemotron-70B-Instruct\"}],\"spaces\":[{\"description\":\"A + leaderboard to compare different open-source text generation models based + on various benchmarks.\",\"id\":\"open-llm-leaderboard/open_llm_leaderboard\"},{\"description\":\"A + leaderboard for comparing chain-of-thought performance of models.\",\"id\":\"logikon/open_cot_leaderboard\"},{\"description\":\"An + text generation based application based on a very powerful LLaMA2 model.\",\"id\":\"ysharma/Explore_llamav2_with_TGI\"},{\"description\":\"An + text generation based application to converse with Zephyr model.\",\"id\":\"HuggingFaceH4/zephyr-chat\"},{\"description\":\"A + leaderboard that ranks text generation models based on blind votes from people.\",\"id\":\"lmsys/chatbot-arena-leaderboard\"},{\"description\":\"An + chatbot to converse with a very powerful text generation model.\",\"id\":\"mlabonne/phixtral-chat\"}],\"summary\":\"Generating + text is the task of generating new text given another text. These models can, + for example, fill in incomplete text or paraphrase.\",\"widgetModels\":[\"mistralai/Mistral-Nemo-Instruct-2407\"],\"youtubeId\":\"e9gNEAlsOvU\",\"id\":\"text-generation\",\"label\":\"Text + Generation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-image\":{\"datasets\":[{\"description\":\"RedCaps + is a large-scale dataset of 12M image-text pairs collected from Reddit.\",\"id\":\"red_caps\"},{\"description\":\"Conceptual + Captions is a dataset consisting of ~3.3M images annotated with captions.\",\"id\":\"conceptual_captions\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"A + city above clouds, pastel colors, Victorian style\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"image.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Inception Score (IS) measure assesses diversity and meaningfulness. It uses + a generated image sample to predict its label. A higher score signifies more + diverse and meaningful images.\",\"id\":\"IS\"},{\"description\":\"The Fr\xE9chet + Inception Distance (FID) calculates the distance between distributions between + synthetic and real samples. A lower FID score indicates better similarity + between the distributions of real and generated images.\",\"id\":\"FID\"},{\"description\":\"R-precision + assesses how the generated image aligns with the provided text description. + It uses the generated images as queries to retrieve relevant text descriptions. + The top 'r' relevant descriptions are selected and used to calculate R-precision + as r/R, where 'R' is the number of ground truth descriptions associated with + the generated images. A higher R-precision value indicates a better model.\",\"id\":\"R-Precision\"}],\"models\":[{\"description\":\"One + of the most powerful image generation models that can generate realistic outputs.\",\"id\":\"black-forest-labs/FLUX.1-dev\"},{\"description\":\"A + powerful yet fast image generation model.\",\"id\":\"latent-consistency/lcm-lora-sdxl\"},{\"description\":\"Text-to-image + model for photorealistic generation.\",\"id\":\"Kwai-Kolors/Kolors\"},{\"description\":\"A + powerful text-to-image model.\",\"id\":\"stabilityai/stable-diffusion-3-medium-diffusers\"}],\"spaces\":[{\"description\":\"A + powerful text-to-image application.\",\"id\":\"stabilityai/stable-diffusion-3-medium\"},{\"description\":\"A + text-to-image application to generate comics.\",\"id\":\"jbilcke-hf/ai-comic-factory\"},{\"description\":\"An + application to match multiple custom image generation models.\",\"id\":\"multimodalart/flux-lora-lab\"},{\"description\":\"A + powerful yet very fast image generation application.\",\"id\":\"latent-consistency/lcm-lora-for-sdxl\"},{\"description\":\"A + gallery to explore various text-to-image models.\",\"id\":\"multimodalart/LoraTheExplorer\"},{\"description\":\"An + application for `text-to-image`, `image-to-image` and image inpainting.\",\"id\":\"ArtGAN/Stable-Diffusion-ControlNet-WebUI\"},{\"description\":\"An + application to generate realistic images given photos of a person and a prompt.\",\"id\":\"InstantX/InstantID\"}],\"summary\":\"Text-to-image + is the task of generating images from input text. These pipelines can also + be used to modify and edit images based on text prompts.\",\"widgetModels\":[\"black-forest-labs/FLUX.1-dev\"],\"youtubeId\":\"\",\"id\":\"text-to-image\",\"label\":\"Text-to-Image\",\"libraries\":[\"diffusers\"]},\"text-to-speech\":{\"canonicalId\":\"text-to-audio\",\"datasets\":[{\"description\":\"10K + hours of multi-speaker English dataset.\",\"id\":\"parler-tts/mls_eng_10k\"},{\"description\":\"Multi-speaker + English dataset.\",\"id\":\"mythicinfinity/libritts_r\"},{\"description\":\"Mulit-lingual + dataset.\",\"id\":\"facebook/multilingual_librispeech\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love audio models on the Hub!\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"audio.wav\",\"type\":\"audio\"}]},\"metrics\":[{\"description\":\"The + Mel Cepstral Distortion (MCD) metric is used to calculate the quality of generated + speech.\",\"id\":\"mel cepstral distortion\"}],\"models\":[{\"description\":\"A + prompt based, powerful TTS model.\",\"id\":\"parler-tts/parler-tts-large-v1\"},{\"description\":\"A + powerful TTS model that supports English and Chinese.\",\"id\":\"SWivid/F5-TTS\"},{\"description\":\"A + massively multi-lingual TTS model.\",\"id\":\"coqui/XTTS-v2\"},{\"description\":\"A + powerful TTS model.\",\"id\":\"amphion/MaskGCT\"},{\"description\":\"A Llama + based TTS model.\",\"id\":\"OuteAI/OuteTTS-0.1-350M\"}],\"spaces\":[{\"description\":\"An + application for generate highly realistic, multilingual speech.\",\"id\":\"suno/bark\"},{\"description\":\"An + application on XTTS, a voice generation model that lets you clone voices into + different languages.\",\"id\":\"coqui/xtts\"},{\"description\":\"An application + that generates speech in different styles in English and Chinese.\",\"id\":\"mrfakename/E2-F5-TTS\"},{\"description\":\"An + application that synthesizes emotional speech for diverse speaker prompts.\",\"id\":\"parler-tts/parler-tts-expresso\"}],\"summary\":\"Text-to-Speech + (TTS) is the task of generating natural sounding speech given text input. + TTS models can be extended to have a single model that generates speech for + multiple speakers and multiple languages.\",\"widgetModels\":[\"suno/bark\"],\"youtubeId\":\"NW62DpzJ274\",\"id\":\"text-to-speech\",\"label\":\"Text-to-Speech\",\"libraries\":[\"espnet\",\"tensorflowtts\",\"transformers\",\"transformers.js\"]},\"text-to-video\":{\"datasets\":[{\"description\":\"Microsoft + Research Video to Text is a large-scale dataset for open domain video captioning\",\"id\":\"iejMac/CLIP-MSR-VTT\"},{\"description\":\"UCF101 + Human Actions dataset consists of 13,320 video clips from YouTube, with 101 + classes.\",\"id\":\"quchenyuan/UCF101-ZIP\"},{\"description\":\"A high-quality + dataset for human action recognition in YouTube videos.\",\"id\":\"nateraw/kinetics\"},{\"description\":\"A + dataset of video clips of humans performing pre-defined basic actions with + everyday objects.\",\"id\":\"HuggingFaceM4/something_something_v2\"},{\"description\":\"This + dataset consists of text-video pairs and contains noisy samples with irrelevant + video descriptions\",\"id\":\"HuggingFaceM4/webvid\"},{\"description\":\"A + dataset of short Flickr videos for the temporal localization of events with + descriptions.\",\"id\":\"iejMac/CLIP-DiDeMo\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Darth + Vader is surfing on the waves.\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"text-to-video-output.gif\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"Inception + Score uses an image classification model that predicts class labels and evaluates + how distinct and diverse the images are. A higher score indicates better video + generation.\",\"id\":\"is\"},{\"description\":\"Frechet Inception Distance + uses an image classification model to obtain image embeddings. The metric + compares mean and standard deviation of the embeddings of real and generated + images. A smaller score indicates better video generation.\",\"id\":\"fid\"},{\"description\":\"Frechet + Video Distance uses a model that captures coherence for changes in frames + and the quality of each frame. A smaller score indicates better video generation.\",\"id\":\"fvd\"},{\"description\":\"CLIPSIM + measures similarity between video frames and text using an image-text similarity + model. A higher score indicates better video generation.\",\"id\":\"clipsim\"}],\"models\":[{\"description\":\"A + strong model for consistent video generation.\",\"id\":\"rain1011/pyramid-flow-sd3\"},{\"description\":\"A + robust model for text-to-video generation.\",\"id\":\"VideoCrafter/VideoCrafter2\"},{\"description\":\"A + cutting-edge text-to-video generation model.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"}],\"spaces\":[{\"description\":\"An + application that generates video from text.\",\"id\":\"VideoCrafter/VideoCrafter\"},{\"description\":\"Consistent + video generation application.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"},{\"description\":\"A + cutting edge video generation application.\",\"id\":\"Pyramid-Flow/pyramid-flow\"}],\"summary\":\"Text-to-video + models can be used in any application that requires generating consistent + sequence of images from text. \",\"widgetModels\":[],\"id\":\"text-to-video\",\"label\":\"Text-to-Video\",\"libraries\":[\"diffusers\"]},\"token-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset useful to benchmark named entity recognition models.\",\"id\":\"eriktks/conll2003\"},{\"description\":\"A + multilingual dataset of Wikipedia articles annotated for named entity recognition + in over 150 different languages.\",\"id\":\"unimelb-nlp/wikiann\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"text\":\"My + name is Omar and I live in Z\xFCrich.\",\"tokens\":[{\"type\":\"PERSON\",\"start\":11,\"end\":15},{\"type\":\"GPE\",\"start\":30,\"end\":36}],\"type\":\"text-with-tokens\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust performance model to identify people, locations, organizations and + names of miscellaneous entities.\",\"id\":\"dslim/bert-base-NER\"},{\"description\":\"A + strong model to identify people, locations, organizations and names in multiple + languages.\",\"id\":\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"},{\"description\":\"A + token classification model specialized on medical entity recognition.\",\"id\":\"blaze999/Medical-NER\"},{\"description\":\"Flair + models are typically the state of the art in named entity recognition tasks.\",\"id\":\"flair/ner-english\"}],\"spaces\":[{\"description\":\"An + application that can recognizes entities, extracts noun chunks and recognizes + various linguistic features of each token.\",\"id\":\"spacy/gradio_pipeline_visualizer\"}],\"summary\":\"Token + classification is a natural language understanding task in which a label is + assigned to some tokens in a text. Some popular token classification subtasks + are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models + could be trained to identify specific entities in a text, such as dates, individuals + and places; and PoS tagging would identify, for example, which words in a + text are verbs, nouns, and punctuation marks.\",\"widgetModels\":[\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"],\"youtubeId\":\"wVHdVlPScxA\",\"id\":\"token-classification\",\"label\":\"Token + Classification\",\"libraries\":[\"adapter-transformers\",\"flair\",\"spacy\",\"span-marker\",\"stanza\",\"transformers\",\"transformers.js\"]},\"translation\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"A + dataset of copyright-free books translated into 16 different languages.\",\"id\":\"Helsinki-NLP/opus_books\"},{\"description\":\"An + example of translation between programming languages. This dataset consists + of functions in Java and C#.\",\"id\":\"google/code_x_glue_cc_code_to_code_trans\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Mein + Name ist Omar und ich wohne in Z\xFCrich.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"BLEU + score is calculated by counting the number of shared single or subsequent + tokens between the generated sequence and the reference. Subsequent n tokens + are called \u201Cn-grams\u201D. Unigram refers to a single token while bi-gram + refers to token pairs and n-grams refer to n subsequent tokens. The score + ranges from 0 to 1, where 1 means the translation perfectly matched and 0 + did not match at all\",\"id\":\"bleu\"},{\"description\":\"\",\"id\":\"sacrebleu\"}],\"models\":[{\"description\":\"Very + powerful model that can translate many languages between each other, especially + low-resource languages.\",\"id\":\"facebook/nllb-200-1.3B\"},{\"description\":\"A + general-purpose Transformer that can be used to translate from English to + German, French, or Romanian.\",\"id\":\"google-t5/t5-base\"}],\"spaces\":[{\"description\":\"An + application that can translate between 100 languages.\",\"id\":\"Iker/Translate-100-languages\"},{\"description\":\"An + application that can translate between many languages.\",\"id\":\"Geonmo/nllb-translation-demo\"}],\"summary\":\"Translation + is the task of converting text from one language to another.\",\"widgetModels\":[\"facebook/mbart-large-50-many-to-many-mmt\"],\"youtubeId\":\"1JvfrvZgi6c\",\"id\":\"translation\",\"label\":\"Translation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"unconditional-image-generation\":{\"datasets\":[{\"description\":\"The + CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with + 600 images per class.\",\"id\":\"cifar100\"},{\"description\":\"Multiple images + of celebrities, used for facial expression translation.\",\"id\":\"CelebA\"}],\"demo\":{\"inputs\":[{\"label\":\"Seed\",\"content\":\"42\",\"type\":\"text\"},{\"label\":\"Number + of images to generate:\",\"content\":\"4\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"unconditional-image-generation-output.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + inception score (IS) evaluates the quality of generated images. It measures + the diversity of the generated images (the model predictions are evenly distributed + across all possible labels) and their 'distinction' or 'sharpness' (the model + confidently predicts a single label for each image).\",\"id\":\"Inception + score (IS)\"},{\"description\":\"The Fr\xE9chet Inception Distance (FID) evaluates + the quality of images created by a generative model by calculating the distance + between feature vectors for real and generated images.\",\"id\":\"Fre\u0107het + Inception Distance (FID)\"}],\"models\":[{\"description\":\"High-quality image + generation model trained on the CIFAR-10 dataset. It synthesizes images of + the ten classes presented in the dataset using diffusion probabilistic models, + a class of latent variable models inspired by considerations from nonequilibrium + thermodynamics.\",\"id\":\"google/ddpm-cifar10-32\"},{\"description\":\"High-quality + image generation model trained on the 256x256 CelebA-HQ dataset. It synthesizes + images of faces using diffusion probabilistic models, a class of latent variable + models inspired by considerations from nonequilibrium thermodynamics.\",\"id\":\"google/ddpm-celebahq-256\"}],\"spaces\":[{\"description\":\"An + application that can generate realistic faces.\",\"id\":\"CompVis/celeba-latent-diffusion\"}],\"summary\":\"Unconditional + image generation is the task of generating images with no condition in any + context (like a prompt text or another image). Once trained, the model will + create images that resemble its training data distribution.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"unconditional-image-generation\",\"label\":\"Unconditional + Image Generation\",\"libraries\":[\"diffusers\"]},\"video-text-to-text\":{\"datasets\":[{\"description\":\"Multiple-choice + questions and answers about videos.\",\"id\":\"lmms-lab/Video-MME\"},{\"description\":\"A + dataset of instructions and question-answer pairs about videos.\",\"id\":\"lmms-lab/VideoChatGPT\"},{\"description\":\"Large + video understanding dataset.\",\"id\":\"HuggingFaceFV/finevideo\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-text-to-text-input.gif\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"What is happening in this video?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + video shows a series of images showing a fountain with water jets and a variety + of colorful flowers and butterflies in the background.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/llava-onevision-qwen2-72b-ov-hf\"},{\"description\":\"Large + and powerful video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/LLaVA-NeXT-Video-34B-hf\"}],\"spaces\":[{\"description\":\"An + application to chat with a video-text-to-text model.\",\"id\":\"llava-hf/video-llava\"},{\"description\":\"A + leaderboard for various video-text-to-text models.\",\"id\":\"opencompass/openvlm_video_leaderboard\"}],\"summary\":\"Video-text-to-text + models take in a video and a text prompt and output text. These models are + also called video-language models.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"video-text-to-text\",\"label\":\"Video-Text-to-Text\",\"libraries\":[\"transformers\"]},\"visual-question-answering\":{\"datasets\":[{\"description\":\"A + widely used dataset containing questions (with answers) about images.\",\"id\":\"Graphcore/vqa\"},{\"description\":\"A + dataset to benchmark visual reasoning based on text in images.\",\"id\":\"facebook/textvqa\"}],\"demo\":{\"inputs\":[{\"filename\":\"elephant.jpeg\",\"type\":\"img\"},{\"label\":\"Question\",\"content\":\"What + is in this image?\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"elephant\",\"score\":0.97},{\"label\":\"elephants\",\"score\":0.06},{\"label\":\"animal\",\"score\":0.003}]}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"Measures + how much a predicted answer differs from the ground truth based on the difference + in their semantic meaning.\",\"id\":\"wu-palmer similarity\"}],\"models\":[{\"description\":\"A + visual question answering model trained to convert charts and plots to text.\",\"id\":\"google/deplot\"},{\"description\":\"A + visual question answering model trained for mathematical reasoning and chart + derendering from images.\",\"id\":\"google/matcha-base\"},{\"description\":\"A + strong visual question answering that answers questions from book covers.\",\"id\":\"google/pix2struct-ocrvqa-large\"}],\"spaces\":[{\"description\":\"An + application that compares visual question answering models across different + tasks.\",\"id\":\"merve/pix2struct\"},{\"description\":\"An application that + can answer questions based on images.\",\"id\":\"nielsr/vilt-vqa\"},{\"description\":\"An + application that can caption images and answer questions about a given image. + \",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An application that can + caption images and answer questions about a given image. \",\"id\":\"vumichien/Img2Prompt\"}],\"summary\":\"Visual + Question Answering is the task of answering open-ended questions based on + an image. They output natural language responses to natural language questions.\",\"widgetModels\":[\"dandelin/vilt-b32-finetuned-vqa\"],\"youtubeId\":\"\",\"id\":\"visual-question-answering\",\"label\":\"Visual + Question Answering\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"The + Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced + collection of 433k sentence pairs annotated with textual entailment information.\",\"id\":\"nyu-mll/multi_nli\"},{\"description\":\"FEVER + is a publicly available dataset for fact extraction and verification against + textual sources.\",\"id\":\"fever/fever\"}],\"demo\":{\"inputs\":[{\"label\":\"Text + Input\",\"content\":\"Dune is the best movie ever.\",\"type\":\"text\"},{\"label\":\"Candidate + Labels\",\"content\":\"CINEMA, ART, MUSIC\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"CINEMA\",\"score\":0.9},{\"label\":\"ART\",\"score\":0.1},{\"label\":\"MUSIC\",\"score\":0}]}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + zero-shot text classification model.\",\"id\":\"facebook/bart-large-mnli\"},{\"description\":\"Powerful + zero-shot multilingual text classification model that can accomplish multiple + tasks.\",\"id\":\"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7\"}],\"spaces\":[],\"summary\":\"Zero-shot + text classification is a task in natural language processing where a model + is trained on a set of labeled examples but is then able to classify new examples + from previously unseen classes.\",\"widgetModels\":[\"facebook/bart-large-mnli\"],\"id\":\"zero-shot-classification\",\"label\":\"Zero-Shot + Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-image-classification\":{\"datasets\":[{\"description\":\"\",\"id\":\"\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-classification-input.jpeg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Cat\",\"score\":0.664},{\"label\":\"Dog\",\"score\":0.329},{\"label\":\"Bird\",\"score\":0.008}]}]},\"metrics\":[{\"description\":\"Computes + the number of times the correct label appears in top K labels predicted\",\"id\":\"top-K + accuracy\"}],\"models\":[{\"description\":\"Robust image classification model + trained on publicly available image-caption data.\",\"id\":\"openai/clip-vit-base-patch16\"},{\"description\":\"Strong + zero-shot image classification model.\",\"id\":\"google/siglip-so400m-patch14-224\"},{\"description\":\"Small + yet powerful zero-shot image classification model that can run on edge devices.\",\"id\":\"apple/MobileCLIP-S1-OpenCLIP\"},{\"description\":\"Strong + image classification model for biomedical domain.\",\"id\":\"microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224\"}],\"spaces\":[{\"description\":\"An + application that leverages zero-shot image classification to find best captions + to generate an image. \",\"id\":\"pharma/CLIP-Interrogator\"},{\"description\":\"An + application to compare different zero-shot image classification models. \",\"id\":\"merve/compare_clip_siglip\"}],\"summary\":\"Zero-shot + image classification is the task of classifying previously unseen classes + during training of a model.\",\"widgetModels\":[\"google/siglip-so400m-patch14-224\"],\"youtubeId\":\"\",\"id\":\"zero-shot-image-classification\",\"label\":\"Zero-Shot + Image Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-object-detection\":{\"datasets\":[],\"demo\":{\"inputs\":[{\"filename\":\"zero-shot-object-detection-input.jpg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"zero-shot-object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + zero-shot object detection model.\",\"id\":\"IDEA-Research/grounding-dino-base\"},{\"description\":\"Cutting-edge + zero-shot object detection model.\",\"id\":\"google/owlv2-base-patch16-ensemble\"}],\"spaces\":[{\"description\":\"A + demo to try the state-of-the-art zero-shot object detection model, OWLv2.\",\"id\":\"merve/owlv2\"},{\"description\":\"A + demo that combines a zero-shot object detection and mask generation model + for zero-shot segmentation.\",\"id\":\"merve/OWLSAM\"}],\"summary\":\"Zero-shot + object detection is a computer vision task to detect objects and their classes + in images, without any prior training or knowledge of the classes. Zero-shot + object detection models receive an image as input, as well as a list of candidate + classes, and output the bounding boxes and labels where the objects have been + detected.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"zero-shot-object-detection\",\"label\":\"Zero-Shot + Object Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"Descriptive + captions for 3D objects in Objaverse.\",\"id\":\"tiange/Cap3D\"}],\"demo\":{\"inputs\":[{\"label\":\"Prompt\",\"content\":\"a + cat statue\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"text-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Text-to-3D + mesh model by OpenAI\",\"id\":\"openai/shap-e\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Text-to-3D + demo with mesh outputs.\",\"id\":\"hysts/Shap-E\"},{\"description\":\"Text/image-to-3D + demo with splat outputs.\",\"id\":\"ashawkey/LGM\"}],\"summary\":\"Text-to-3D + models take in text input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"text-to-3d\",\"label\":\"Text-to-3D\",\"libraries\":[\"diffusers\"]},\"image-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"A + dataset of isolated object images for evaluating image-to-3D models.\",\"id\":\"dylanebert/iso3d\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-3d-image-input.png\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"image-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Fast + image-to-3D mesh model by Tencent.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Fast + image-to-3D mesh model by StabilityAI\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"A + scaled up image-to-3D mesh model derived from TripoSR.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate image-to-3D models.\",\"id\":\"dylanebert/3d-arena\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Image-to-3D + demo with splat outputs.\",\"id\":\"dylanebert/LGM-mini\"}],\"summary\":\"Image-to-3D + models take in image input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"image-to-3d\",\"label\":\"Image-to-3D\",\"libraries\":[\"diffusers\"]}}" + headers: + Access-Control-Allow-Origin: + - https://huggingface.co + Access-Control-Expose-Headers: + - X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range,X-Xet-Access-Token,X-Xet-Token-Expiration,X-Xet-Refresh-Route,X-Xet-Cas-Url,X-Xet-Hash + Connection: + - keep-alive + Content-Length: + - '74829' + Content-Type: + - application/json; charset=utf-8 + Date: + - Fri, 10 Jan 2025 20:10:07 GMT + ETag: + - W/"1244d-rY02FKAQRZwcoD2xI/AqH93OPBg" + Referrer-Policy: + - strict-origin-when-cross-origin + Vary: + - Origin + Via: + - 1.1 fd31d1c67e8343bcdb7b55426d326fe6.cloudfront.net (CloudFront) + X-Amz-Cf-Id: + - Y1xgQvkL6Wr2UKrU4WAcib21vFYjEJcMh_FmaNEuUZQKpvHrI6XlyA== + X-Amz-Cf-Pop: + - BOG50-C1 + X-Cache: + - Miss from cloudfront + X-Powered-By: + - huggingface-moon + X-Request-Id: + - 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+ headers: + Access-Control-Allow-Credentials: + - 'true' + Access-Control-Expose-Headers: + - x-compute-type, x-compute-time + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 10 Jan 2025 20:10:09 GMT + Server: + - uvicorn + Transfer-Encoding: + - chunked + Vary: + - Origin, Access-Control-Request-Method, Access-Control-Request-Headers + x-compute-characters: + - '-1' + x-compute-time: + - '0.686' + x-compute-type: + - cpu + x-request-id: + - 9eDnNk_7Wf3UJnODx6sKx + x-sha: + - 489d5cd81a0b59fab9b7ea758d3548ebe99677da + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_huggingface_hub_object_detection.yaml b/tests/cassettes/test_huggingface_hub_object_detection.yaml new file mode 100644 index 0000000..93fbbf6 --- /dev/null +++ b/tests/cassettes/test_huggingface_hub_object_detection.yaml @@ -0,0 +1,32459 @@ +interactions: +- request: + body: !!python/object/new:_io.BytesIO + state: !!python/tuple + - !!binary | + 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Each probability distribution is the distribution of predicted + words\",\"id\":\"cross_entropy\"},{\"description\":\"Perplexity is the exponential + of the cross-entropy loss. It evaluates the probabilities assigned to the + next word by the model. Lower perplexity indicates better performance\",\"id\":\"perplexity\"}],\"models\":[{\"description\":\"The + famous BERT model.\",\"id\":\"google-bert/bert-base-uncased\"},{\"description\":\"A + multilingual model trained on 100 languages.\",\"id\":\"FacebookAI/xlm-roberta-base\"}],\"spaces\":[],\"summary\":\"Masked + language modeling is the task of masking some of the words in a sentence and + predicting which words should replace those masks. These models are useful + when we want to get a statistical understanding of the language in which the + model is trained in.\",\"widgetModels\":[\"distilroberta-base\"],\"youtubeId\":\"mqElG5QJWUg\",\"id\":\"fill-mask\",\"label\":\"Fill-Mask\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"image-classification\":{\"datasets\":[{\"description\":\"Benchmark + dataset used for image classification with images that belong to 100 classes.\",\"id\":\"cifar100\"},{\"description\":\"Dataset + consisting of images of garments.\",\"id\":\"fashion_mnist\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-classification-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Egyptian + cat\",\"score\":0.514},{\"label\":\"Tabby cat\",\"score\":0.193},{\"label\":\"Tiger + cat\",\"score\":0.068}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + strong image classification model.\",\"id\":\"google/vit-base-patch16-224\"},{\"description\":\"A + robust image classification model.\",\"id\":\"facebook/deit-base-distilled-patch16-224\"},{\"description\":\"A + strong image classification model.\",\"id\":\"facebook/convnext-large-224\"}],\"spaces\":[{\"description\":\"An + application that classifies what a given image is about.\",\"id\":\"nielsr/perceiver-image-classification\"}],\"summary\":\"Image + classification is the task of assigning a label or class to an entire image. + Images are expected to have only one class for each image. Image classification + models take an image as input and return a prediction about which class the + image belongs to.\",\"widgetModels\":[\"google/vit-base-patch16-224\"],\"youtubeId\":\"tjAIM7BOYhw\",\"id\":\"image-classification\",\"label\":\"Image + Classification\",\"libraries\":[\"keras\",\"timm\",\"transformers\",\"transformers.js\"]},\"image-feature-extraction\":{\"datasets\":[{\"description\":\"ImageNet-1K + is a image classification dataset in which images are used to train image-feature-extraction + models.\",\"id\":\"imagenet-1k\"}],\"demo\":{\"inputs\":[{\"filename\":\"mask-generation-input.png\",\"type\":\"img\"}],\"outputs\":[{\"table\":[[\"Dimension + 1\",\"Dimension 2\",\"Dimension 3\"],[\"0.21236686408519745\",\"1.0919708013534546\",\"0.8512550592422485\"],[\"0.809657871723175\",\"-0.18544459342956543\",\"-0.7851548194885254\"],[\"1.3103108406066895\",\"-0.2479034662246704\",\"-0.9107287526130676\"],[\"1.8536205291748047\",\"-0.36419737339019775\",\"0.09717650711536407\"]],\"type\":\"tabular\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + powerful image feature extraction model.\",\"id\":\"timm/vit_large_patch14_dinov2.lvd142m\"},{\"description\":\"A + strong image feature extraction model.\",\"id\":\"nvidia/MambaVision-T-1K\"},{\"description\":\"A + robust image feature extraction model.\",\"id\":\"facebook/dino-vitb16\"},{\"description\":\"Strong + image feature extraction model made for information retrieval from documents.\",\"id\":\"vidore/colpali\"},{\"description\":\"Strong + image feature extraction model that can be used on images and documents.\",\"id\":\"OpenGVLab/InternViT-6B-448px-V1-2\"}],\"spaces\":[],\"summary\":\"Image + feature extraction is the task of extracting features learnt in a computer + vision model.\",\"widgetModels\":[],\"id\":\"image-feature-extraction\",\"label\":\"Image + Feature Extraction\",\"libraries\":[\"timm\",\"transformers\"]},\"image-segmentation\":{\"datasets\":[{\"description\":\"Scene + segmentation dataset.\",\"id\":\"scene_parse_150\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-segmentation-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"image-segmentation-output.png\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"Average + Precision (AP) is the Area Under the PR Curve (AUC-PR). It is calculated for + each semantic class separately\",\"id\":\"Average Precision\"},{\"description\":\"Mean + Average Precision (mAP) is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"Intersection over Union (IoU) is the + overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic + classes\",\"id\":\"Mean Intersection over Union\"},{\"description\":\"AP\u03B1 + is the Average Precision at the IoU threshold of a \u03B1 value, for example, + AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + semantic segmentation model trained on ADE20k.\",\"id\":\"openmmlab/upernet-convnext-small\"},{\"description\":\"Background + removal model.\",\"id\":\"briaai/RMBG-1.4\"},{\"description\":\"A multipurpose + image segmentation model for high resolution images.\",\"id\":\"ZhengPeng7/BiRefNet\"},{\"description\":\"Powerful + human-centric image segmentation model.\",\"id\":\"facebook/sapiens-seg-1b\"},{\"description\":\"Panoptic + segmentation model trained on the COCO (common objects) dataset.\",\"id\":\"facebook/mask2former-swin-large-coco-panoptic\"}],\"spaces\":[{\"description\":\"A + semantic segmentation application that can predict unseen instances out of + the box.\",\"id\":\"facebook/ov-seg\"},{\"description\":\"One of the strongest + segmentation applications.\",\"id\":\"jbrinkma/segment-anything\"},{\"description\":\"A + human-centric segmentation model.\",\"id\":\"facebook/sapiens-pose\"},{\"description\":\"An + instance segmentation application to predict neuronal cell types from microscopy + images.\",\"id\":\"rashmi/sartorius-cell-instance-segmentation\"},{\"description\":\"An + application that segments videos.\",\"id\":\"ArtGAN/Segment-Anything-Video\"},{\"description\":\"An + panoptic segmentation application built for outdoor environments.\",\"id\":\"segments/panoptic-segment-anything\"}],\"summary\":\"Image + Segmentation divides an image into segments where each pixel in the image + is mapped to an object. This task has multiple variants such as instance segmentation, + panoptic segmentation and semantic segmentation.\",\"widgetModels\":[\"nvidia/segformer-b0-finetuned-ade-512-512\"],\"youtubeId\":\"dKE8SIt9C-w\",\"id\":\"image-segmentation\",\"label\":\"Image + Segmentation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"image-to-image\":{\"datasets\":[{\"description\":\"Synthetic + dataset, for image relighting\",\"id\":\"VIDIT\"},{\"description\":\"Multiple + images of celebrities, used for facial expression translation\",\"id\":\"huggan/CelebA-faces\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-image-input.jpeg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"image-to-image-output.png\",\"type\":\"img\"}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"Peak + Signal to Noise Ratio (PSNR) is an approximation of the human perception, + considering the ratio of the absolute intensity with respect to the variations. + Measured in dB, a high value indicates a high fidelity.\",\"id\":\"PSNR\"},{\"description\":\"Structural + Similarity Index (SSIM) is a perceptual metric which compares the luminance, + contrast and structure of two images. The values of SSIM range between -1 + and 1, and higher values indicate closer resemblance to the original image.\",\"id\":\"SSIM\"},{\"description\":\"Inception + Score (IS) is an analysis of the labels predicted by an image classification + model when presented with a sample of the generated images.\",\"id\":\"IS\"}],\"models\":[{\"description\":\"An + image-to-image model to improve image resolution.\",\"id\":\"fal/AuraSR-v2\"},{\"description\":\"A + model that increases the resolution of an image.\",\"id\":\"keras-io/super-resolution\"},{\"description\":\"A + model that creates a set of variations of the input image in the style of + DALL-E using Stable Diffusion.\",\"id\":\"lambdalabs/sd-image-variations-diffusers\"},{\"description\":\"A + model that generates images based on segments in the input image and the text + prompt.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"A + model that takes an image and an instruction to edit the image.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"spaces\":[{\"description\":\"Image + enhancer application for low light.\",\"id\":\"keras-io/low-light-image-enhancement\"},{\"description\":\"Style + transfer application.\",\"id\":\"keras-io/neural-style-transfer\"},{\"description\":\"An + application that generates images based on segment control.\",\"id\":\"mfidabel/controlnet-segment-anything\"},{\"description\":\"Image + generation application that takes image control and text prompt.\",\"id\":\"hysts/ControlNet\"},{\"description\":\"Colorize + any image using this app.\",\"id\":\"ioclab/brightness-controlnet\"},{\"description\":\"Edit + images with instructions.\",\"id\":\"timbrooks/instruct-pix2pix\"}],\"summary\":\"Image-to-image + is the task of transforming an input image through a variety of possible manipulations + and enhancements, such as super-resolution, image inpainting, colorization, + and more.\",\"widgetModels\":[\"stabilityai/stable-diffusion-2-inpainting\"],\"youtubeId\":\"\",\"id\":\"image-to-image\",\"label\":\"Image-to-Image\",\"libraries\":[\"diffusers\",\"transformers\",\"transformers.js\"]},\"image-text-to-text\":{\"datasets\":[{\"description\":\"Instructions + composed of image and text.\",\"id\":\"liuhaotian/LLaVA-Instruct-150K\"},{\"description\":\"Conversation + turns where questions involve image and text.\",\"id\":\"liuhaotian/LLaVA-Pretrain\"},{\"description\":\"A + collection of datasets made for model fine-tuning.\",\"id\":\"HuggingFaceM4/the_cauldron\"},{\"description\":\"Screenshots + of websites with their HTML/CSS codes.\",\"id\":\"HuggingFaceM4/WebSight\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-text-to-text-input.png\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"Describe the position of the bee in detail.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + bee is sitting on a pink flower, surrounded by other flowers. The bee is positioned + in the center of the flower, with its head and front legs sticking out.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + vision language model with great visual understanding and reasoning capabilities.\",\"id\":\"meta-llama/Llama-3.2-11B-Vision-Instruct\"},{\"description\":\"Cutting-edge + vision language models.\",\"id\":\"allenai/Molmo-7B-D-0924\"},{\"description\":\"Small + yet powerful model.\",\"id\":\"vikhyatk/moondream2\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"Qwen/Qwen2-VL-7B-Instruct\"},{\"description\":\"Strong + image-text-to-text model.\",\"id\":\"mistralai/Pixtral-12B-2409\"},{\"description\":\"Strong + image-text-to-text model focused on documents.\",\"id\":\"stepfun-ai/GOT-OCR2_0\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate vision language models.\",\"id\":\"opencompass/open_vlm_leaderboard\"},{\"description\":\"Vision + language models arena, where models are ranked by votes of users.\",\"id\":\"WildVision/vision-arena\"},{\"description\":\"Powerful + vision-language model assistant.\",\"id\":\"akhaliq/Molmo-7B-D-0924\"},{\"description\":\"An + image-text-to-text application focused on documents.\",\"id\":\"stepfun-ai/GOT_official_online_demo\"},{\"description\":\"An + application to compare outputs of different vision language models.\",\"id\":\"merve/compare_VLMs\"},{\"description\":\"An + application for chatting with an image-text-to-text model.\",\"id\":\"GanymedeNil/Qwen2-VL-7B\"}],\"summary\":\"Image-text-to-text + models take in an image and text prompt and output text. These models are + also called vision-language models, or VLMs. The difference from image-to-text + models is that these models take an additional text input, not restricting + the model to certain use cases like image captioning, and may also be trained + to accept a conversation as input.\",\"widgetModels\":[\"meta-llama/Llama-3.2-11B-Vision-Instruct\"],\"youtubeId\":\"IoGaGfU1CIg\",\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"libraries\":[\"transformers\"]},\"image-to-text\":{\"datasets\":[{\"description\":\"Dataset + from 12M image-text of Reddit\",\"id\":\"red_caps\"},{\"description\":\"Dataset + from 3.3M images of Google\",\"id\":\"datasets/conceptual_captions\"}],\"demo\":{\"inputs\":[{\"filename\":\"savanna.jpg\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Detailed + description\",\"content\":\"a herd of giraffes and zebras grazing in a field\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust image captioning model.\",\"id\":\"Salesforce/blip2-opt-2.7b\"},{\"description\":\"A + powerful and accurate image-to-text model that can also localize concepts + in images.\",\"id\":\"microsoft/kosmos-2-patch14-224\"},{\"description\":\"A + strong optical character recognition model.\",\"id\":\"facebook/nougat-base\"},{\"description\":\"A + powerful model that lets you have a conversation with the image.\",\"id\":\"llava-hf/llava-1.5-7b-hf\"}],\"spaces\":[{\"description\":\"An + application that compares various image captioning models.\",\"id\":\"nielsr/comparing-captioning-models\"},{\"description\":\"A + robust image captioning application.\",\"id\":\"flax-community/image-captioning\"},{\"description\":\"An + application that transcribes handwritings into text.\",\"id\":\"nielsr/TrOCR-handwritten\"},{\"description\":\"An + application that can caption images and answer questions about a given image.\",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An + application that can caption images and answer questions with a conversational + agent.\",\"id\":\"Salesforce/BLIP2\"},{\"description\":\"An image captioning + application that demonstrates the effect of noise on captions.\",\"id\":\"johko/capdec-image-captioning\"}],\"summary\":\"Image + to text models output a text from a given image. Image captioning or optical + character recognition can be considered as the most common applications of + image to text.\",\"widgetModels\":[\"Salesforce/blip-image-captioning-large\"],\"youtubeId\":\"\",\"id\":\"image-to-text\",\"label\":\"Image-to-Text\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"keypoint-detection\":{\"datasets\":[{\"description\":\"A + dataset of hand keypoints of over 500k examples.\",\"id\":\"Vincent-luo/hagrid-mediapipe-hands\"}],\"demo\":{\"inputs\":[{\"filename\":\"keypoint-detection-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"keypoint-detection-output.png\",\"type\":\"img\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust keypoint detection model.\",\"id\":\"magic-leap-community/superpoint\"},{\"description\":\"Strong + keypoint detection model used to detect human pose.\",\"id\":\"facebook/sapiens-pose-1b\"}],\"spaces\":[{\"description\":\"An + application that detects hand keypoints in real-time.\",\"id\":\"datasciencedojo/Hand-Keypoint-Detection-Realtime\"},{\"description\":\"An + application to try a universal keypoint detection model.\",\"id\":\"merve/SuperPoint\"}],\"summary\":\"Keypoint + detection is the task of identifying meaningful distinctive points or features + in an image.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"keypoint-detection\",\"label\":\"Keypoint + Detection\",\"libraries\":[\"transformers\"]},\"mask-generation\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple Vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Medical + Imaging dataset of the Human Brain for segmentation and mask generating tasks\",\"id\":\"rocky93/BraTS_segmentation\"}],\"demo\":{\"inputs\":[{\"filename\":\"mask-generation-input.png\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"mask-generation-output.png\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"IoU + is used to measure the overlap between predicted mask and the ground truth + mask.\",\"id\":\"Intersection over Union (IoU)\"}],\"models\":[{\"description\":\"Small + yet powerful mask generation model.\",\"id\":\"Zigeng/SlimSAM-uniform-50\"},{\"description\":\"Very + strong mask generation model.\",\"id\":\"facebook/sam2-hiera-large\"}],\"spaces\":[{\"description\":\"An + application that combines a mask generation model with a zero-shot object + detection model for text-guided image segmentation.\",\"id\":\"merve/OWLSAM2\"},{\"description\":\"An + application that compares the performance of a large and a small mask generation + model.\",\"id\":\"merve/slimsam\"},{\"description\":\"An application based + on an improved mask generation model.\",\"id\":\"SkalskiP/segment-anything-model-2\"},{\"description\":\"An + application to remove objects from videos using mask generation models.\",\"id\":\"SkalskiP/SAM_and_ProPainter\"}],\"summary\":\"Mask + generation is the task of generating masks that identify a specific object + or region of interest in a given image. Masks are often used in segmentation + tasks, where they provide a precise way to isolate the object of interest + for further processing or analysis.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"mask-generation\",\"label\":\"Mask + Generation\",\"libraries\":[\"transformers\"]},\"object-detection\":{\"datasets\":[{\"description\":\"Widely + used benchmark dataset for multiple vision tasks.\",\"id\":\"merve/coco2017\"},{\"description\":\"Multi-task + computer vision benchmark.\",\"id\":\"merve/pascal-voc\"}],\"demo\":{\"inputs\":[{\"filename\":\"object-detection-input.jpg\",\"type\":\"img\"}],\"outputs\":[{\"filename\":\"object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + object detection model pre-trained on the COCO 2017 dataset.\",\"id\":\"facebook/detr-resnet-50\"},{\"description\":\"Real-time + and accurate object detection model.\",\"id\":\"jameslahm/yolov10x\"},{\"description\":\"Fast + and accurate object detection model trained on COCO and Object365 datasets.\",\"id\":\"PekingU/rtdetr_r18vd_coco_o365\"}],\"spaces\":[{\"description\":\"Leaderboard + to compare various object detection models across several metrics.\",\"id\":\"hf-vision/object_detection_leaderboard\"},{\"description\":\"An + application that contains various object detection models to try from.\",\"id\":\"Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS\"},{\"description\":\"An + application that shows multiple cutting edge techniques for object detection + and tracking.\",\"id\":\"kadirnar/torchyolo\"},{\"description\":\"An object + tracking, segmentation and inpainting application.\",\"id\":\"VIPLab/Track-Anything\"},{\"description\":\"Very + fast object tracking application based on object detection.\",\"id\":\"merve/RT-DETR-tracking-coco\"}],\"summary\":\"Object + Detection models allow users to identify objects of certain defined classes. + Object detection models receive an image as input and output the images with + bounding boxes and labels on detected objects.\",\"widgetModels\":[\"facebook/detr-resnet-50\"],\"youtubeId\":\"WdAeKSOpxhw\",\"id\":\"object-detection\",\"label\":\"Object + Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"video-classification\":{\"datasets\":[{\"description\":\"Benchmark + dataset used for video classification with videos that belong to 400 classes.\",\"id\":\"kinetics400\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-classification-input.gif\",\"type\":\"img\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Playing + Guitar\",\"score\":0.514},{\"label\":\"Playing Tennis\",\"score\":0.193},{\"label\":\"Cooking\",\"score\":0.068}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"google/vivit-b-16x2-kinetics400\"},{\"description\":\"Strong + Video Classification model trained on the Kinetics 400 dataset.\",\"id\":\"microsoft/xclip-base-patch32\"}],\"spaces\":[{\"description\":\"An + application that classifies video at different timestamps.\",\"id\":\"nateraw/lavila\"},{\"description\":\"An + application that classifies video.\",\"id\":\"fcakyon/video-classification\"}],\"summary\":\"Video + classification is the task of assigning a label or class to an entire video. + Videos are expected to have only one class for each video. Video classification + models take a video as input and return a prediction about which class the + video belongs to.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"video-classification\",\"label\":\"Video + Classification\",\"libraries\":[\"transformers\"]},\"question-answering\":{\"datasets\":[{\"description\":\"A + famous question answering dataset based on English articles from Wikipedia.\",\"id\":\"squad_v2\"},{\"description\":\"A + dataset of aggregated anonymized actual queries issued to the Google search + engine.\",\"id\":\"natural_questions\"}],\"demo\":{\"inputs\":[{\"label\":\"Question\",\"content\":\"Which + name is also used to describe the Amazon rainforest in English?\",\"type\":\"text\"},{\"label\":\"Context\",\"content\":\"The + Amazon rainforest, also known in English as Amazonia or the Amazon Jungle\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"Amazonia\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Exact + Match is a metric based on the strict character match of the predicted answer + and the right answer. For answers predicted correctly, the Exact Match will + be 1. Even if only one character is different, Exact Match will be 0\",\"id\":\"exact-match\"},{\"description\":\" + The F1-Score metric is useful if we value both false positives and false negatives + equally. The F1-Score is calculated on each word in the predicted sequence + against the correct answer\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust baseline model for most question answering domains.\",\"id\":\"deepset/roberta-base-squad2\"},{\"description\":\"Small + yet robust model that can answer questions.\",\"id\":\"distilbert/distilbert-base-cased-distilled-squad\"},{\"description\":\"A + special model that can answer questions from tables.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that can answer a long question from Wikipedia.\",\"id\":\"deepset/wikipedia-assistant\"}],\"summary\":\"Question + Answering models can retrieve the answer to a question from a given text, + which is useful for searching for an answer in a document. Some question answering + models can generate answers without context!\",\"widgetModels\":[\"deepset/roberta-base-squad2\"],\"youtubeId\":\"ajPx5LwJD-I\",\"id\":\"question-answering\",\"label\":\"Question + Answering\",\"libraries\":[\"adapter-transformers\",\"allennlp\",\"transformers\",\"transformers.js\"]},\"reinforcement-learning\":{\"datasets\":[{\"description\":\"A + curation of widely used datasets for Data Driven Deep Reinforcement Learning + (D4RL)\",\"id\":\"edbeeching/decision_transformer_gym_replay\"}],\"demo\":{\"inputs\":[{\"label\":\"State\",\"content\":\"Red + traffic light, pedestrians are about to pass.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Action\",\"content\":\"Stop + the car.\",\"type\":\"text\"},{\"label\":\"Next State\",\"content\":\"Yellow + light, pedestrians have crossed.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Accumulated + reward across all time steps discounted by a factor that ranges between 0 + and 1 and determines how much the agent optimizes for future relative to immediate + rewards. Measures how good is the policy ultimately found by a given algorithm + considering uncertainty over the future.\",\"id\":\"Discounted Total Reward\"},{\"description\":\"Average + return obtained after running the policy for a certain number of evaluation + episodes. As opposed to total reward, mean reward considers how much reward + a given algorithm receives while learning.\",\"id\":\"Mean Reward\"},{\"description\":\"Measures + how good a given algorithm is after a predefined time. Some algorithms may + be guaranteed to converge to optimal behavior across many time steps. However, + an agent that reaches an acceptable level of optimality after a given time + horizon may be preferable to one that ultimately reaches optimality but takes + a long time.\",\"id\":\"Level of Performance After Some Time\"}],\"models\":[{\"description\":\"A + Reinforcement Learning model trained on expert data from the Gym Hopper environment\",\"id\":\"edbeeching/decision-transformer-gym-hopper-expert\"},{\"description\":\"A + PPO agent playing seals/CartPole-v0 using the stable-baselines3 library and + the RL Zoo.\",\"id\":\"HumanCompatibleAI/ppo-seals-CartPole-v0\"}],\"spaces\":[{\"description\":\"An + application for a cute puppy agent learning to catch a stick.\",\"id\":\"ThomasSimonini/Huggy\"},{\"description\":\"An + application to play Snowball Fight with a reinforcement learning agent.\",\"id\":\"ThomasSimonini/SnowballFight\"}],\"summary\":\"Reinforcement + learning is the computational approach of learning from action by interacting + with an environment through trial and error and receiving rewards (negative + or positive) as feedback\",\"widgetModels\":[],\"youtubeId\":\"q0BiUn5LiBc\",\"id\":\"reinforcement-learning\",\"label\":\"Reinforcement + Learning\",\"libraries\":[\"transformers\",\"stable-baselines3\",\"ml-agents\",\"sample-factory\"]},\"sentence-similarity\":{\"datasets\":[{\"description\":\"Bing + queries with relevant passages from various web sources.\",\"id\":\"ms_marco\"}],\"demo\":{\"inputs\":[{\"label\":\"Source + sentence\",\"content\":\"Machine learning is so easy.\",\"type\":\"text\"},{\"label\":\"Sentences + to compare to\",\"content\":\"Deep learning is so straightforward.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"This + is so difficult, like rocket science.\",\"type\":\"text\"},{\"label\":\"\",\"content\":\"I + can't believe how much I struggled with this.\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Deep + learning is so straightforward.\",\"score\":0.623},{\"label\":\"This is so + difficult, like rocket science.\",\"score\":0.413},{\"label\":\"I can't believe + how much I struggled with this.\",\"score\":0.256}]}]},\"metrics\":[{\"description\":\"Reciprocal + Rank is a measure used to rank the relevancy of documents given a set of documents. + Reciprocal Rank is the reciprocal of the rank of the document retrieved, meaning, + if the rank is 3, the Reciprocal Rank is 0.33. If the rank is 1, the Reciprocal + Rank is 1\",\"id\":\"Mean Reciprocal Rank\"},{\"description\":\"The similarity + of the embeddings is evaluated mainly on cosine similarity. It is calculated + as the cosine of the angle between two vectors. It is particularly useful + when your texts are not the same length\",\"id\":\"Cosine Similarity\"}],\"models\":[{\"description\":\"This + model works well for sentences and paragraphs and can be used for clustering/grouping + and semantic searches.\",\"id\":\"sentence-transformers/all-mpnet-base-v2\"},{\"description\":\"A + multilingual robust sentence similarity model..\",\"id\":\"BAAI/bge-m3\"}],\"spaces\":[{\"description\":\"An + application that leverages sentence similarity to answer questions from YouTube + videos.\",\"id\":\"Gradio-Blocks/Ask_Questions_To_YouTube_Videos\"},{\"description\":\"An + application that retrieves relevant PubMed abstracts for a given online article + which can be used as further references.\",\"id\":\"Gradio-Blocks/pubmed-abstract-retriever\"},{\"description\":\"An + application that leverages sentence similarity to summarize text.\",\"id\":\"nickmuchi/article-text-summarizer\"},{\"description\":\"A + guide that explains how Sentence Transformers can be used for semantic search.\",\"id\":\"sentence-transformers/Sentence_Transformers_for_semantic_search\"}],\"summary\":\"Sentence + Similarity is the task of determining how similar two texts are. Sentence + similarity models convert input texts into vectors (embeddings) that capture + semantic information and calculate how close (similar) they are between them. + This task is particularly useful for information retrieval and clustering/grouping.\",\"widgetModels\":[\"BAAI/bge-small-en-v1.5\"],\"youtubeId\":\"VCZq5AkbNEU\",\"id\":\"sentence-similarity\",\"label\":\"Sentence + Similarity\",\"libraries\":[\"sentence-transformers\",\"spacy\",\"transformers.js\"]},\"summarization\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"News + articles in five different languages along with their summaries. Widely used + for benchmarking multilingual summarization models.\",\"id\":\"mlsum\"},{\"description\":\"English + conversations and their summaries. Useful for benchmarking conversational + agents.\",\"id\":\"samsum\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building, and the tallest structure in Paris. Its base is square, measuring + 125 metres (410 ft) on each side. It was the first structure to reach a height + of 300 metres. Excluding transmitters, the Eiffel Tower is the second tallest + free-standing structure in France after the Millau Viaduct.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"The + tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey + building. It was the first structure to reach a height of 300 metres.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"The + generated sequence is compared against its summary, and the overlap of tokens + are counted. ROUGE-N refers to overlap of N subsequent tokens, ROUGE-1 refers + to overlap of single tokens and ROUGE-2 is the overlap of two subsequent tokens.\",\"id\":\"rouge\"}],\"models\":[{\"description\":\"A + strong summarization model trained on English news articles. Excels at generating + factual summaries.\",\"id\":\"facebook/bart-large-cnn\"},{\"description\":\"A + summarization model trained on medical articles.\",\"id\":\"Falconsai/medical_summarization\"}],\"spaces\":[{\"description\":\"An + application that can summarize long paragraphs.\",\"id\":\"pszemraj/summarize-long-text\"},{\"description\":\"A + much needed summarization application for terms and conditions.\",\"id\":\"ml6team/distilbart-tos-summarizer-tosdr\"},{\"description\":\"An + application that summarizes long documents.\",\"id\":\"pszemraj/document-summarization\"},{\"description\":\"An + application that can detect errors in abstractive summarization.\",\"id\":\"ml6team/post-processing-summarization\"}],\"summary\":\"Summarization + is the task of producing a shorter version of a document while preserving + its important information. Some models can extract text from the original + input, while other models can generate entirely new text.\",\"widgetModels\":[\"facebook/bart-large-cnn\"],\"youtubeId\":\"yHnr5Dk2zCI\",\"id\":\"summarization\",\"label\":\"Summarization\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"table-question-answering\":{\"datasets\":[{\"description\":\"The + WikiTableQuestions dataset is a large-scale dataset for the task of question + answering on semi-structured tables.\",\"id\":\"wikitablequestions\"},{\"description\":\"WikiSQL + is a dataset of 80654 hand-annotated examples of questions and SQL queries + distributed across 24241 tables from Wikipedia.\",\"id\":\"wikisql\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Rank\",\"Name\",\"No.of + reigns\",\"Combined days\"],[\"1\",\"lou Thesz\",\"3\",\"3749\"],[\"2\",\"Ric + Flair\",\"8\",\"3103\"],[\"3\",\"Harley Race\",\"7\",\"1799\"]],\"type\":\"tabular\"},{\"label\":\"Question\",\"content\":\"What + is the number of reigns for Harley Race?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"7\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Checks + whether the predicted answer(s) is the same as the ground-truth answer(s).\",\"id\":\"Denotation + Accuracy\"}],\"models\":[{\"description\":\"A table question answering model + that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL + query on a given table.\",\"id\":\"microsoft/tapex-base\"},{\"description\":\"A + robust table question answering model.\",\"id\":\"google/tapas-base-finetuned-wtq\"}],\"spaces\":[{\"description\":\"An + application that answers questions based on table CSV files.\",\"id\":\"katanaml/table-query\"}],\"summary\":\"Table + Question Answering (Table QA) is the answering a question about an information + on a given table.\",\"widgetModels\":[\"google/tapas-base-finetuned-wtq\"],\"id\":\"table-question-answering\",\"label\":\"Table + Question Answering\",\"libraries\":[\"transformers\"]},\"tabular-classification\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Glucose\",\"Blood + Pressure \",\"Skin Thickness\",\"Insulin\",\"BMI\"],[\"148\",\"72\",\"35\",\"0\",\"33.6\"],[\"150\",\"50\",\"30\",\"0\",\"35.1\"],[\"141\",\"60\",\"29\",\"1\",\"39.2\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"Diabetes\"],[\"1\"],[\"1\"],[\"0\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"Breast + cancer prediction model based on decision trees.\",\"id\":\"scikit-learn/cancer-prediction-trees\"}],\"spaces\":[{\"description\":\"An + application that can predict defective products on a production line.\",\"id\":\"scikit-learn/tabular-playground\"},{\"description\":\"An + application that compares various tabular classification techniques on different + datasets.\",\"id\":\"scikit-learn/classification\"}],\"summary\":\"Tabular + classification is the task of classifying a target category (a group) based + on set of attributes.\",\"widgetModels\":[\"scikit-learn/tabular-playground\"],\"youtubeId\":\"\",\"id\":\"tabular-classification\",\"label\":\"Tabular + Classification\",\"libraries\":[\"sklearn\"]},\"tabular-regression\":{\"datasets\":[{\"description\":\"A + comprehensive curation of datasets covering all benchmarks.\",\"id\":\"inria-soda/tabular-benchmark\"}],\"demo\":{\"inputs\":[{\"table\":[[\"Car + Name\",\"Horsepower\",\"Weight\"],[\"ford torino\",\"140\",\"3,449\"],[\"amc + hornet\",\"97\",\"2,774\"],[\"toyota corolla\",\"65\",\"1,773\"]],\"type\":\"tabular\"}],\"outputs\":[{\"table\":[[\"MPG + (miles per gallon)\"],[\"17\"],[\"18\"],[\"31\"]],\"type\":\"tabular\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"mse\"},{\"description\":\"Coefficient + of determination (or R-squared) is a measure of how well the model fits the + data. Higher R-squared is considered a better fit.\",\"id\":\"r-squared\"}],\"models\":[{\"description\":\"Fish + weight prediction based on length measurements and species.\",\"id\":\"scikit-learn/Fish-Weight\"}],\"spaces\":[{\"description\":\"An + application that can predict weight of a fish based on set of attributes.\",\"id\":\"scikit-learn/fish-weight-prediction\"}],\"summary\":\"Tabular + regression is the task of predicting a numerical value given a set of attributes.\",\"widgetModels\":[\"scikit-learn/Fish-Weight\"],\"youtubeId\":\"\",\"id\":\"tabular-regression\",\"label\":\"Tabular + Regression\",\"libraries\":[\"sklearn\"]},\"text-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"A + text classification dataset used to benchmark natural language inference models\",\"id\":\"stanfordnlp/snli\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love Hugging Face!\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"POSITIVE\",\"score\":0.9},{\"label\":\"NEUTRAL\",\"score\":0.1},{\"label\":\"NEGATIVE\",\"score\":0}]}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"The + F1 metric is the harmonic mean of the precision and recall. It can be calculated + as: F1 = 2 * (precision * recall) / (precision + recall)\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust model trained for sentiment analysis.\",\"id\":\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"},{\"description\":\"A + sentiment analysis model specialized in financial sentiment.\",\"id\":\"ProsusAI/finbert\"},{\"description\":\"A + sentiment analysis model specialized in analyzing tweets.\",\"id\":\"cardiffnlp/twitter-roberta-base-sentiment-latest\"},{\"description\":\"A + model that can classify languages.\",\"id\":\"papluca/xlm-roberta-base-language-detection\"},{\"description\":\"A + model that can classify text generation attacks.\",\"id\":\"meta-llama/Prompt-Guard-86M\"}],\"spaces\":[{\"description\":\"An + application that can classify financial sentiment.\",\"id\":\"IoannisTr/Tech_Stocks_Trading_Assistant\"},{\"description\":\"A + dashboard that contains various text classification tasks.\",\"id\":\"miesnerjacob/Multi-task-NLP\"},{\"description\":\"An + application that analyzes user reviews in healthcare.\",\"id\":\"spacy/healthsea-demo\"}],\"summary\":\"Text + Classification is the task of assigning a label or class to a given text. + Some use cases are sentiment analysis, natural language inference, and assessing + grammatical correctness.\",\"widgetModels\":[\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\"],\"youtubeId\":\"leNG9fN9FQU\",\"id\":\"text-classification\",\"label\":\"Text + Classification\",\"libraries\":[\"adapter-transformers\",\"setfit\",\"spacy\",\"transformers\",\"transformers.js\"]},\"text-generation\":{\"datasets\":[{\"description\":\"A + large multilingual dataset of text crawled from the web.\",\"id\":\"mc4\"},{\"description\":\"Diverse + open-source data consisting of 22 smaller high-quality datasets. It was used + to train GPT-Neo.\",\"id\":\"the_pile\"},{\"description\":\"Truly open-source, + curated and cleaned dialogue dataset.\",\"id\":\"HuggingFaceH4/ultrachat_200k\"},{\"description\":\"An + instruction dataset with preference ratings on responses.\",\"id\":\"openbmb/UltraFeedback\"},{\"description\":\"A + large synthetic dataset for alignment of text generation models.\",\"id\":\"argilla/magpie-ultra-v0.1\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Once + upon a time,\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Once + upon a time, we knew that our ancestors were on the verge of extinction. The + great explorers and poets of the Old World, from Alexander the Great to Chaucer, + are dead and gone. A good many of our ancient explorers and poets have\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"Cross + Entropy is a metric that calculates the difference between two probability + distributions. Each probability distribution is the distribution of predicted + words\",\"id\":\"Cross Entropy\"},{\"description\":\"The Perplexity metric + is the exponential of the cross-entropy loss. It evaluates the probabilities + assigned to the next word by the model. Lower perplexity indicates better + performance\",\"id\":\"Perplexity\"}],\"models\":[{\"description\":\"A text-generation + model trained to follow instructions.\",\"id\":\"google/gemma-2-2b-it\"},{\"description\":\"Very + powerful text generation model trained to follow instructions.\",\"id\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\"},{\"description\":\"Small + yet powerful text generation model.\",\"id\":\"microsoft/Phi-3-mini-4k-instruct\"},{\"description\":\"A + very powerful model that can solve mathematical problems.\",\"id\":\"AI-MO/NuminaMath-7B-TIR\"},{\"description\":\"Strong + text generation model to follow instructions.\",\"id\":\"Qwen/Qwen2.5-7B-Instruct\"},{\"description\":\"Very + strong open-source large language model.\",\"id\":\"nvidia/Llama-3.1-Nemotron-70B-Instruct\"}],\"spaces\":[{\"description\":\"A + leaderboard to compare different open-source text generation models based + on various benchmarks.\",\"id\":\"open-llm-leaderboard/open_llm_leaderboard\"},{\"description\":\"A + leaderboard for comparing chain-of-thought performance of models.\",\"id\":\"logikon/open_cot_leaderboard\"},{\"description\":\"An + text generation based application based on a very powerful LLaMA2 model.\",\"id\":\"ysharma/Explore_llamav2_with_TGI\"},{\"description\":\"An + text generation based application to converse with Zephyr model.\",\"id\":\"HuggingFaceH4/zephyr-chat\"},{\"description\":\"A + leaderboard that ranks text generation models based on blind votes from people.\",\"id\":\"lmsys/chatbot-arena-leaderboard\"},{\"description\":\"An + chatbot to converse with a very powerful text generation model.\",\"id\":\"mlabonne/phixtral-chat\"}],\"summary\":\"Generating + text is the task of generating new text given another text. These models can, + for example, fill in incomplete text or paraphrase.\",\"widgetModels\":[\"mistralai/Mistral-Nemo-Instruct-2407\"],\"youtubeId\":\"e9gNEAlsOvU\",\"id\":\"text-generation\",\"label\":\"Text + Generation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-image\":{\"datasets\":[{\"description\":\"RedCaps + is a large-scale dataset of 12M image-text pairs collected from Reddit.\",\"id\":\"red_caps\"},{\"description\":\"Conceptual + Captions is a dataset consisting of ~3.3M images annotated with captions.\",\"id\":\"conceptual_captions\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"A + city above clouds, pastel colors, Victorian style\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"image.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Inception Score (IS) measure assesses diversity and meaningfulness. It uses + a generated image sample to predict its label. A higher score signifies more + diverse and meaningful images.\",\"id\":\"IS\"},{\"description\":\"The Fr\xE9chet + Inception Distance (FID) calculates the distance between distributions between + synthetic and real samples. A lower FID score indicates better similarity + between the distributions of real and generated images.\",\"id\":\"FID\"},{\"description\":\"R-precision + assesses how the generated image aligns with the provided text description. + It uses the generated images as queries to retrieve relevant text descriptions. + The top 'r' relevant descriptions are selected and used to calculate R-precision + as r/R, where 'R' is the number of ground truth descriptions associated with + the generated images. A higher R-precision value indicates a better model.\",\"id\":\"R-Precision\"}],\"models\":[{\"description\":\"One + of the most powerful image generation models that can generate realistic outputs.\",\"id\":\"black-forest-labs/FLUX.1-dev\"},{\"description\":\"A + powerful yet fast image generation model.\",\"id\":\"latent-consistency/lcm-lora-sdxl\"},{\"description\":\"Text-to-image + model for photorealistic generation.\",\"id\":\"Kwai-Kolors/Kolors\"},{\"description\":\"A + powerful text-to-image model.\",\"id\":\"stabilityai/stable-diffusion-3-medium-diffusers\"}],\"spaces\":[{\"description\":\"A + powerful text-to-image application.\",\"id\":\"stabilityai/stable-diffusion-3-medium\"},{\"description\":\"A + text-to-image application to generate comics.\",\"id\":\"jbilcke-hf/ai-comic-factory\"},{\"description\":\"An + application to match multiple custom image generation models.\",\"id\":\"multimodalart/flux-lora-lab\"},{\"description\":\"A + powerful yet very fast image generation application.\",\"id\":\"latent-consistency/lcm-lora-for-sdxl\"},{\"description\":\"A + gallery to explore various text-to-image models.\",\"id\":\"multimodalart/LoraTheExplorer\"},{\"description\":\"An + application for `text-to-image`, `image-to-image` and image inpainting.\",\"id\":\"ArtGAN/Stable-Diffusion-ControlNet-WebUI\"},{\"description\":\"An + application to generate realistic images given photos of a person and a prompt.\",\"id\":\"InstantX/InstantID\"}],\"summary\":\"Text-to-image + is the task of generating images from input text. These pipelines can also + be used to modify and edit images based on text prompts.\",\"widgetModels\":[\"black-forest-labs/FLUX.1-dev\"],\"youtubeId\":\"\",\"id\":\"text-to-image\",\"label\":\"Text-to-Image\",\"libraries\":[\"diffusers\"]},\"text-to-speech\":{\"canonicalId\":\"text-to-audio\",\"datasets\":[{\"description\":\"10K + hours of multi-speaker English dataset.\",\"id\":\"parler-tts/mls_eng_10k\"},{\"description\":\"Multi-speaker + English dataset.\",\"id\":\"mythicinfinity/libritts_r\"},{\"description\":\"Mulit-lingual + dataset.\",\"id\":\"facebook/multilingual_librispeech\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"I + love audio models on the Hub!\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"audio.wav\",\"type\":\"audio\"}]},\"metrics\":[{\"description\":\"The + Mel Cepstral Distortion (MCD) metric is used to calculate the quality of generated + speech.\",\"id\":\"mel cepstral distortion\"}],\"models\":[{\"description\":\"A + prompt based, powerful TTS model.\",\"id\":\"parler-tts/parler-tts-large-v1\"},{\"description\":\"A + powerful TTS model that supports English and Chinese.\",\"id\":\"SWivid/F5-TTS\"},{\"description\":\"A + massively multi-lingual TTS model.\",\"id\":\"coqui/XTTS-v2\"},{\"description\":\"A + powerful TTS model.\",\"id\":\"amphion/MaskGCT\"},{\"description\":\"A Llama + based TTS model.\",\"id\":\"OuteAI/OuteTTS-0.1-350M\"}],\"spaces\":[{\"description\":\"An + application for generate highly realistic, multilingual speech.\",\"id\":\"suno/bark\"},{\"description\":\"An + application on XTTS, a voice generation model that lets you clone voices into + different languages.\",\"id\":\"coqui/xtts\"},{\"description\":\"An application + that generates speech in different styles in English and Chinese.\",\"id\":\"mrfakename/E2-F5-TTS\"},{\"description\":\"An + application that synthesizes emotional speech for diverse speaker prompts.\",\"id\":\"parler-tts/parler-tts-expresso\"}],\"summary\":\"Text-to-Speech + (TTS) is the task of generating natural sounding speech given text input. + TTS models can be extended to have a single model that generates speech for + multiple speakers and multiple languages.\",\"widgetModels\":[\"suno/bark\"],\"youtubeId\":\"NW62DpzJ274\",\"id\":\"text-to-speech\",\"label\":\"Text-to-Speech\",\"libraries\":[\"espnet\",\"tensorflowtts\",\"transformers\",\"transformers.js\"]},\"text-to-video\":{\"datasets\":[{\"description\":\"Microsoft + Research Video to Text is a large-scale dataset for open domain video captioning\",\"id\":\"iejMac/CLIP-MSR-VTT\"},{\"description\":\"UCF101 + Human Actions dataset consists of 13,320 video clips from YouTube, with 101 + classes.\",\"id\":\"quchenyuan/UCF101-ZIP\"},{\"description\":\"A high-quality + dataset for human action recognition in YouTube videos.\",\"id\":\"nateraw/kinetics\"},{\"description\":\"A + dataset of video clips of humans performing pre-defined basic actions with + everyday objects.\",\"id\":\"HuggingFaceM4/something_something_v2\"},{\"description\":\"This + dataset consists of text-video pairs and contains noisy samples with irrelevant + video descriptions\",\"id\":\"HuggingFaceM4/webvid\"},{\"description\":\"A + dataset of short Flickr videos for the temporal localization of events with + descriptions.\",\"id\":\"iejMac/CLIP-DiDeMo\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"Darth + Vader is surfing on the waves.\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"text-to-video-output.gif\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"Inception + Score uses an image classification model that predicts class labels and evaluates + how distinct and diverse the images are. A higher score indicates better video + generation.\",\"id\":\"is\"},{\"description\":\"Frechet Inception Distance + uses an image classification model to obtain image embeddings. The metric + compares mean and standard deviation of the embeddings of real and generated + images. A smaller score indicates better video generation.\",\"id\":\"fid\"},{\"description\":\"Frechet + Video Distance uses a model that captures coherence for changes in frames + and the quality of each frame. A smaller score indicates better video generation.\",\"id\":\"fvd\"},{\"description\":\"CLIPSIM + measures similarity between video frames and text using an image-text similarity + model. A higher score indicates better video generation.\",\"id\":\"clipsim\"}],\"models\":[{\"description\":\"A + strong model for consistent video generation.\",\"id\":\"rain1011/pyramid-flow-sd3\"},{\"description\":\"A + robust model for text-to-video generation.\",\"id\":\"VideoCrafter/VideoCrafter2\"},{\"description\":\"A + cutting-edge text-to-video generation model.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"}],\"spaces\":[{\"description\":\"An + application that generates video from text.\",\"id\":\"VideoCrafter/VideoCrafter\"},{\"description\":\"Consistent + video generation application.\",\"id\":\"TIGER-Lab/T2V-Turbo-V2\"},{\"description\":\"A + cutting edge video generation application.\",\"id\":\"Pyramid-Flow/pyramid-flow\"}],\"summary\":\"Text-to-video + models can be used in any application that requires generating consistent + sequence of images from text. \",\"widgetModels\":[],\"id\":\"text-to-video\",\"label\":\"Text-to-Video\",\"libraries\":[\"diffusers\"]},\"token-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset useful to benchmark named entity recognition models.\",\"id\":\"eriktks/conll2003\"},{\"description\":\"A + multilingual dataset of Wikipedia articles annotated for named entity recognition + in over 150 different languages.\",\"id\":\"unimelb-nlp/wikiann\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"text\":\"My + name is Omar and I live in Z\xFCrich.\",\"tokens\":[{\"type\":\"PERSON\",\"start\":11,\"end\":15},{\"type\":\"GPE\",\"start\":30,\"end\":36}],\"type\":\"text-with-tokens\"}]},\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"\",\"id\":\"recall\"},{\"description\":\"\",\"id\":\"precision\"},{\"description\":\"\",\"id\":\"f1\"}],\"models\":[{\"description\":\"A + robust performance model to identify people, locations, organizations and + names of miscellaneous entities.\",\"id\":\"dslim/bert-base-NER\"},{\"description\":\"A + strong model to identify people, locations, organizations and names in multiple + languages.\",\"id\":\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"},{\"description\":\"A + token classification model specialized on medical entity recognition.\",\"id\":\"blaze999/Medical-NER\"},{\"description\":\"Flair + models are typically the state of the art in named entity recognition tasks.\",\"id\":\"flair/ner-english\"}],\"spaces\":[{\"description\":\"An + application that can recognizes entities, extracts noun chunks and recognizes + various linguistic features of each token.\",\"id\":\"spacy/gradio_pipeline_visualizer\"}],\"summary\":\"Token + classification is a natural language understanding task in which a label is + assigned to some tokens in a text. Some popular token classification subtasks + are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models + could be trained to identify specific entities in a text, such as dates, individuals + and places; and PoS tagging would identify, for example, which words in a + text are verbs, nouns, and punctuation marks.\",\"widgetModels\":[\"FacebookAI/xlm-roberta-large-finetuned-conll03-english\"],\"youtubeId\":\"wVHdVlPScxA\",\"id\":\"token-classification\",\"label\":\"Token + Classification\",\"libraries\":[\"adapter-transformers\",\"flair\",\"spacy\",\"span-marker\",\"stanza\",\"transformers\",\"transformers.js\"]},\"translation\":{\"canonicalId\":\"text2text-generation\",\"datasets\":[{\"description\":\"A + dataset of copyright-free books translated into 16 different languages.\",\"id\":\"Helsinki-NLP/opus_books\"},{\"description\":\"An + example of translation between programming languages. This dataset consists + of functions in Java and C#.\",\"id\":\"google/code_x_glue_cc_code_to_code_trans\"}],\"demo\":{\"inputs\":[{\"label\":\"Input\",\"content\":\"My + name is Omar and I live in Z\xFCrich.\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Output\",\"content\":\"Mein + Name ist Omar und ich wohne in Z\xFCrich.\",\"type\":\"text\"}]},\"metrics\":[{\"description\":\"BLEU + score is calculated by counting the number of shared single or subsequent + tokens between the generated sequence and the reference. Subsequent n tokens + are called \u201Cn-grams\u201D. Unigram refers to a single token while bi-gram + refers to token pairs and n-grams refer to n subsequent tokens. The score + ranges from 0 to 1, where 1 means the translation perfectly matched and 0 + did not match at all\",\"id\":\"bleu\"},{\"description\":\"\",\"id\":\"sacrebleu\"}],\"models\":[{\"description\":\"Very + powerful model that can translate many languages between each other, especially + low-resource languages.\",\"id\":\"facebook/nllb-200-1.3B\"},{\"description\":\"A + general-purpose Transformer that can be used to translate from English to + German, French, or Romanian.\",\"id\":\"google-t5/t5-base\"}],\"spaces\":[{\"description\":\"An + application that can translate between 100 languages.\",\"id\":\"Iker/Translate-100-languages\"},{\"description\":\"An + application that can translate between many languages.\",\"id\":\"Geonmo/nllb-translation-demo\"}],\"summary\":\"Translation + is the task of converting text from one language to another.\",\"widgetModels\":[\"facebook/mbart-large-50-many-to-many-mmt\"],\"youtubeId\":\"1JvfrvZgi6c\",\"id\":\"translation\",\"label\":\"Translation\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"unconditional-image-generation\":{\"datasets\":[{\"description\":\"The + CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with + 600 images per class.\",\"id\":\"cifar100\"},{\"description\":\"Multiple images + of celebrities, used for facial expression translation.\",\"id\":\"CelebA\"}],\"demo\":{\"inputs\":[{\"label\":\"Seed\",\"content\":\"42\",\"type\":\"text\"},{\"label\":\"Number + of images to generate:\",\"content\":\"4\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"unconditional-image-generation-output.jpeg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + inception score (IS) evaluates the quality of generated images. It measures + the diversity of the generated images (the model predictions are evenly distributed + across all possible labels) and their 'distinction' or 'sharpness' (the model + confidently predicts a single label for each image).\",\"id\":\"Inception + score (IS)\"},{\"description\":\"The Fr\xE9chet Inception Distance (FID) evaluates + the quality of images created by a generative model by calculating the distance + between feature vectors for real and generated images.\",\"id\":\"Fre\u0107het + Inception Distance (FID)\"}],\"models\":[{\"description\":\"High-quality image + generation model trained on the CIFAR-10 dataset. It synthesizes images of + the ten classes presented in the dataset using diffusion probabilistic models, + a class of latent variable models inspired by considerations from nonequilibrium + thermodynamics.\",\"id\":\"google/ddpm-cifar10-32\"},{\"description\":\"High-quality + image generation model trained on the 256x256 CelebA-HQ dataset. It synthesizes + images of faces using diffusion probabilistic models, a class of latent variable + models inspired by considerations from nonequilibrium thermodynamics.\",\"id\":\"google/ddpm-celebahq-256\"}],\"spaces\":[{\"description\":\"An + application that can generate realistic faces.\",\"id\":\"CompVis/celeba-latent-diffusion\"}],\"summary\":\"Unconditional + image generation is the task of generating images with no condition in any + context (like a prompt text or another image). Once trained, the model will + create images that resemble its training data distribution.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"unconditional-image-generation\",\"label\":\"Unconditional + Image Generation\",\"libraries\":[\"diffusers\"]},\"video-text-to-text\":{\"datasets\":[{\"description\":\"Multiple-choice + questions and answers about videos.\",\"id\":\"lmms-lab/Video-MME\"},{\"description\":\"A + dataset of instructions and question-answer pairs about videos.\",\"id\":\"lmms-lab/VideoChatGPT\"},{\"description\":\"Large + video understanding dataset.\",\"id\":\"HuggingFaceFV/finevideo\"}],\"demo\":{\"inputs\":[{\"filename\":\"video-text-to-text-input.gif\",\"type\":\"img\"},{\"label\":\"Text + Prompt\",\"content\":\"What is happening in this video?\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Answer\",\"content\":\"The + video shows a series of images showing a fountain with water jets and a variety + of colorful flowers and butterflies in the background.\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"A + robust video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/llava-onevision-qwen2-72b-ov-hf\"},{\"description\":\"Large + and powerful video-text-to-text model that can take in image and video inputs.\",\"id\":\"llava-hf/LLaVA-NeXT-Video-34B-hf\"}],\"spaces\":[{\"description\":\"An + application to chat with a video-text-to-text model.\",\"id\":\"llava-hf/video-llava\"},{\"description\":\"A + leaderboard for various video-text-to-text models.\",\"id\":\"opencompass/openvlm_video_leaderboard\"}],\"summary\":\"Video-text-to-text + models take in a video and a text prompt and output text. These models are + also called video-language models.\",\"widgetModels\":[\"\"],\"youtubeId\":\"\",\"id\":\"video-text-to-text\",\"label\":\"Video-Text-to-Text\",\"libraries\":[\"transformers\"]},\"visual-question-answering\":{\"datasets\":[{\"description\":\"A + widely used dataset containing questions (with answers) about images.\",\"id\":\"Graphcore/vqa\"},{\"description\":\"A + dataset to benchmark visual reasoning based on text in images.\",\"id\":\"facebook/textvqa\"}],\"demo\":{\"inputs\":[{\"filename\":\"elephant.jpeg\",\"type\":\"img\"},{\"label\":\"Question\",\"content\":\"What + is in this image?\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"elephant\",\"score\":0.97},{\"label\":\"elephants\",\"score\":0.06},{\"label\":\"animal\",\"score\":0.003}]}]},\"isPlaceholder\":false,\"metrics\":[{\"description\":\"\",\"id\":\"accuracy\"},{\"description\":\"Measures + how much a predicted answer differs from the ground truth based on the difference + in their semantic meaning.\",\"id\":\"wu-palmer similarity\"}],\"models\":[{\"description\":\"A + visual question answering model trained to convert charts and plots to text.\",\"id\":\"google/deplot\"},{\"description\":\"A + visual question answering model trained for mathematical reasoning and chart + derendering from images.\",\"id\":\"google/matcha-base\"},{\"description\":\"A + strong visual question answering that answers questions from book covers.\",\"id\":\"google/pix2struct-ocrvqa-large\"}],\"spaces\":[{\"description\":\"An + application that compares visual question answering models across different + tasks.\",\"id\":\"merve/pix2struct\"},{\"description\":\"An application that + can answer questions based on images.\",\"id\":\"nielsr/vilt-vqa\"},{\"description\":\"An + application that can caption images and answer questions about a given image. + \",\"id\":\"Salesforce/BLIP\"},{\"description\":\"An application that can + caption images and answer questions about a given image. \",\"id\":\"vumichien/Img2Prompt\"}],\"summary\":\"Visual + Question Answering is the task of answering open-ended questions based on + an image. They output natural language responses to natural language questions.\",\"widgetModels\":[\"dandelin/vilt-b32-finetuned-vqa\"],\"youtubeId\":\"\",\"id\":\"visual-question-answering\",\"label\":\"Visual + Question Answering\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-classification\":{\"datasets\":[{\"description\":\"A + widely used dataset used to benchmark multiple variants of text classification.\",\"id\":\"nyu-mll/glue\"},{\"description\":\"The + Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced + collection of 433k sentence pairs annotated with textual entailment information.\",\"id\":\"nyu-mll/multi_nli\"},{\"description\":\"FEVER + is a publicly available dataset for fact extraction and verification against + textual sources.\",\"id\":\"fever/fever\"}],\"demo\":{\"inputs\":[{\"label\":\"Text + Input\",\"content\":\"Dune is the best movie ever.\",\"type\":\"text\"},{\"label\":\"Candidate + Labels\",\"content\":\"CINEMA, ART, MUSIC\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"CINEMA\",\"score\":0.9},{\"label\":\"ART\",\"score\":0.1},{\"label\":\"MUSIC\",\"score\":0}]}]},\"metrics\":[],\"models\":[{\"description\":\"Powerful + zero-shot text classification model.\",\"id\":\"facebook/bart-large-mnli\"},{\"description\":\"Powerful + zero-shot multilingual text classification model that can accomplish multiple + tasks.\",\"id\":\"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7\"}],\"spaces\":[],\"summary\":\"Zero-shot + text classification is a task in natural language processing where a model + is trained on a set of labeled examples but is then able to classify new examples + from previously unseen classes.\",\"widgetModels\":[\"facebook/bart-large-mnli\"],\"id\":\"zero-shot-classification\",\"label\":\"Zero-Shot + Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-image-classification\":{\"datasets\":[{\"description\":\"\",\"id\":\"\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-classification-input.jpeg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"type\":\"chart\",\"data\":[{\"label\":\"Cat\",\"score\":0.664},{\"label\":\"Dog\",\"score\":0.329},{\"label\":\"Bird\",\"score\":0.008}]}]},\"metrics\":[{\"description\":\"Computes + the number of times the correct label appears in top K labels predicted\",\"id\":\"top-K + accuracy\"}],\"models\":[{\"description\":\"Robust image classification model + trained on publicly available image-caption data.\",\"id\":\"openai/clip-vit-base-patch16\"},{\"description\":\"Strong + zero-shot image classification model.\",\"id\":\"google/siglip-so400m-patch14-224\"},{\"description\":\"Small + yet powerful zero-shot image classification model that can run on edge devices.\",\"id\":\"apple/MobileCLIP-S1-OpenCLIP\"},{\"description\":\"Strong + image classification model for biomedical domain.\",\"id\":\"microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224\"}],\"spaces\":[{\"description\":\"An + application that leverages zero-shot image classification to find best captions + to generate an image. \",\"id\":\"pharma/CLIP-Interrogator\"},{\"description\":\"An + application to compare different zero-shot image classification models. \",\"id\":\"merve/compare_clip_siglip\"}],\"summary\":\"Zero-shot + image classification is the task of classifying previously unseen classes + during training of a model.\",\"widgetModels\":[\"google/siglip-so400m-patch14-224\"],\"youtubeId\":\"\",\"id\":\"zero-shot-image-classification\",\"label\":\"Zero-Shot + Image Classification\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"zero-shot-object-detection\":{\"datasets\":[],\"demo\":{\"inputs\":[{\"filename\":\"zero-shot-object-detection-input.jpg\",\"type\":\"img\"},{\"label\":\"Classes\",\"content\":\"cat, + dog, bird\",\"type\":\"text\"}],\"outputs\":[{\"filename\":\"zero-shot-object-detection-output.jpg\",\"type\":\"img\"}]},\"metrics\":[{\"description\":\"The + Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It + is calculated for each class separately\",\"id\":\"Average Precision\"},{\"description\":\"The + Mean Average Precision (mAP) metric is the overall average of the AP values\",\"id\":\"Mean + Average Precision\"},{\"description\":\"The AP\u03B1 metric is the Average + Precision at the IoU threshold of a \u03B1 value, for example, AP50 and AP75\",\"id\":\"AP\u03B1\"}],\"models\":[{\"description\":\"Solid + zero-shot object detection model.\",\"id\":\"IDEA-Research/grounding-dino-base\"},{\"description\":\"Cutting-edge + zero-shot object detection model.\",\"id\":\"google/owlv2-base-patch16-ensemble\"}],\"spaces\":[{\"description\":\"A + demo to try the state-of-the-art zero-shot object detection model, OWLv2.\",\"id\":\"merve/owlv2\"},{\"description\":\"A + demo that combines a zero-shot object detection and mask generation model + for zero-shot segmentation.\",\"id\":\"merve/OWLSAM\"}],\"summary\":\"Zero-shot + object detection is a computer vision task to detect objects and their classes + in images, without any prior training or knowledge of the classes. Zero-shot + object detection models receive an image as input, as well as a list of candidate + classes, and output the bounding boxes and labels where the objects have been + detected.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"zero-shot-object-detection\",\"label\":\"Zero-Shot + Object Detection\",\"libraries\":[\"transformers\",\"transformers.js\"]},\"text-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"Descriptive + captions for 3D objects in Objaverse.\",\"id\":\"tiange/Cap3D\"}],\"demo\":{\"inputs\":[{\"label\":\"Prompt\",\"content\":\"a + cat statue\",\"type\":\"text\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"text-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Text-to-3D + mesh model by OpenAI\",\"id\":\"openai/shap-e\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Text-to-3D + demo with mesh outputs.\",\"id\":\"hysts/Shap-E\"},{\"description\":\"Text/image-to-3D + demo with splat outputs.\",\"id\":\"ashawkey/LGM\"}],\"summary\":\"Text-to-3D + models take in text input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"text-to-3d\",\"label\":\"Text-to-3D\",\"libraries\":[\"diffusers\"]},\"image-to-3d\":{\"datasets\":[{\"description\":\"A + large dataset of over 10 million 3D objects.\",\"id\":\"allenai/objaverse-xl\"},{\"description\":\"A + dataset of isolated object images for evaluating image-to-3D models.\",\"id\":\"dylanebert/iso3d\"}],\"demo\":{\"inputs\":[{\"filename\":\"image-to-3d-image-input.png\",\"type\":\"img\"}],\"outputs\":[{\"label\":\"Result\",\"content\":\"image-to-3d-3d-output-filename.glb\",\"type\":\"text\"}]},\"metrics\":[],\"models\":[{\"description\":\"Fast + image-to-3D mesh model by Tencent.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Fast + image-to-3D mesh model by StabilityAI\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"A + scaled up image-to-3D mesh model derived from TripoSR.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Generative + 3D gaussian splatting model.\",\"id\":\"ashawkey/LGM\"}],\"spaces\":[{\"description\":\"Leaderboard + to evaluate image-to-3D models.\",\"id\":\"dylanebert/3d-arena\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"TencentARC/InstantMesh\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"stabilityai/TripoSR\"},{\"description\":\"Image-to-3D + demo with mesh outputs.\",\"id\":\"hwjiang/Real3D\"},{\"description\":\"Image-to-3D + demo with splat outputs.\",\"id\":\"dylanebert/LGM-mini\"}],\"summary\":\"Image-to-3D + models take in image input and produce 3D output.\",\"widgetModels\":[],\"youtubeId\":\"\",\"id\":\"image-to-3d\",\"label\":\"Image-to-3D\",\"libraries\":[\"diffusers\"]}}" + headers: + Access-Control-Allow-Origin: + - https://huggingface.co + Access-Control-Expose-Headers: + - X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range,X-Xet-Access-Token,X-Xet-Token-Expiration,X-Xet-Refresh-Route,X-Xet-Cas-Url,X-Xet-Hash + Connection: + - keep-alive + Content-Length: + - '74829' + Content-Type: + - application/json; charset=utf-8 + Date: + - Fri, 10 Jan 2025 18:58:20 GMT + ETag: + - W/"1244d-rY02FKAQRZwcoD2xI/AqH93OPBg" + Referrer-Policy: + - strict-origin-when-cross-origin + Vary: + - Origin + Via: + - 1.1 5d916dd63294253ded878b26198e5c76.cloudfront.net (CloudFront) + X-Amz-Cf-Id: + - 7QEIZzFNCC8Eioxu7RB-iuVfsqlNI7U4FS3xNbxpOrxXoc2_xcS4sw== + X-Amz-Cf-Pop: + - BOG50-C1 + X-Cache: + - Miss from cloudfront + X-Powered-By: + - huggingface-moon + X-Request-Id: + - 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+ headers: + Access-Control-Allow-Credentials: + - 'true' + Connection: + - keep-alive + Content-Length: + - '5919' + Content-Type: + - application/json + Date: + - Fri, 10 Jan 2025 18:58:21 GMT + Vary: + - Origin, Access-Control-Request-Method, Access-Control-Request-Headers + x-compute-time: + - '0.657' + x-compute-type: + - cache + x-request-id: + - SzrfhglhybuB2Kr2YjxFR + x-sha: + - 1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b + status: + code: 200 + message: OK +version: 1 diff --git a/tests/conftest.py b/tests/conftest.py index decc0a7..56c5f7e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,7 @@ import os -import pytest + import httpx +import pytest def set_envvar_if_unset(name: str, value: str): diff --git a/tests/data/dog.png b/tests/data/dog.png new file mode 100644 index 0000000..2008d7b Binary files /dev/null and b/tests/data/dog.png differ diff --git a/tests/data/street_scene.png b/tests/data/street_scene.png new file mode 100644 index 0000000..2b3a467 Binary files /dev/null and b/tests/data/street_scene.png differ diff --git a/tests/test_huggingface_tracer.py b/tests/test_huggingface_tracer.py index 698fd06..6684851 100644 --- a/tests/test_huggingface_tracer.py +++ b/tests/test_huggingface_tracer.py @@ -1,6 +1,5 @@ from pathlib import Path - import pytest from huggingface_hub import InferenceClient, AsyncInferenceClient @@ -80,7 +79,6 @@ async def test_huggingface_hub_image_generation_async(tracer_init): @pytest.mark.vcr -@pytest.mark.asyncio def test_huggingface_hub_translation(tracer_init): client = InferenceClient() client.translation( @@ -172,3 +170,82 @@ async def test_huggingface_hub_image_to_image_async(tracer_init): assert response.scope3ai.request.request_duration_ms == 6467 assert response.scope3ai.request.output_images == [Image(root="1024x1024")] assert response.scope3ai.request.input_images == [Image(root="1024x1024")] + + +@pytest.mark.vcr +@pytest.mark.asyncio +async def test_huggingface_hub_object_detection_async(tracer_init): + client = AsyncInferenceClient() + datadir = Path(__file__).parent / "data" + street_scene_image = open((datadir / "street_scene.png").as_posix(), "rb") + street_scene_image_bytes = street_scene_image.read() + response = await client.object_detection(street_scene_image_bytes) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.request.input_images == [Image(root="1024x1024")] + assert response.scope3ai.request.request_duration_ms == 657 + + +@pytest.mark.vcr +def test_huggingface_hub_object_detection(tracer_init): + client = InferenceClient() + datadir = Path(__file__).parent / "data" + response = client.object_detection((datadir / "street_scene.png").as_posix()) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert response.scope3ai.request.input_images == [Image(root="1024x1024")] + assert response.scope3ai.request.request_duration_ms == 657 + + +@pytest.mark.vcr +@pytest.mark.asyncio +async def test_huggingface_hub_image_segmentation_async(tracer_init): + client = AsyncInferenceClient() + datadir = Path(__file__).parent / "data" + dog_image = open((datadir / "dog.png").as_posix(), "rb") + dog_image_bytes = dog_image.read() + response = await client.image_segmentation(dog_image_bytes) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert response.scope3ai.request.input_images == [Image(root="1024x1024")] + assert response.scope3ai.request.request_duration_ms == 686 + + +@pytest.mark.vcr +def test_huggingface_hub_image_segmentation(tracer_init): + client = InferenceClient() + datadir = Path(__file__).parent / "data" + response = client.image_segmentation((datadir / "dog.png").as_posix()) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.request.input_images == [Image(root="1024x1024")] + assert response.scope3ai.request.request_duration_ms == 686 + + +@pytest.mark.vcr +@pytest.mark.asyncio +async def test_huggingface_hub_image_classification_async(tracer_init): + client = AsyncInferenceClient() + datadir = Path(__file__).parent / "data" + cat_image = open((datadir / "cat.png").as_posix(), "rb") + cat_image_bytes = cat_image.read() + response = await client.image_classification(cat_image_bytes) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.request.input_images == [Image(root="1024x704")] + assert response.scope3ai.request.request_duration_ms == 226 + + +@pytest.mark.vcr +def test_huggingface_hub_image_classification(tracer_init): + client = InferenceClient() + datadir = Path(__file__).parent / "data" + response = client.image_classification((datadir / "cat.png").as_posix()) + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.impact is None + assert getattr(response, "scope3ai") is not None + assert response.scope3ai.request.input_images == [Image(root="1024x704")] + assert response.scope3ai.request.request_duration_ms == 226