Pre-trained Deep Learning models and demos (high quality and extremely fast)
-
Updated
Jun 25, 2024 - Python
Pre-trained Deep Learning models and demos (high quality and extremely fast)
🥂 Gracefully face hCaptcha challenge with MoE(ONNX) embedded solution.
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
Run PyTorch models in the browser using ONNX.js
YOLOv7 to detect bone fractures on X-ray images
A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, change opset, change to the specified input order, addition of OP, RGB to BGR conversion, change batch size, batch rename of OP, and JSON convertion for ONNX models.
Count number of parameters / MACs / FLOPS for ONNX models.
🦉Gracefully face reCAPTCHA challenge with ModelHub embedded solution.
🏗 hCaptcha image label binary model factory (PyTorch Training, Cluster-based Auto Label Tools, Export ONNX model, ONNX model inference)
Convert Caffe models to ONNX.
Stable Diffusion UI: Diffusers (CUDA/ONNX)
simple and fast wav2lip using onnx models for face-detection and inference. Easy installation
Full-attention multi-instrumental music transformer featuring asymmetrical encoding with octo-velocity, and chords counters tokens, optimized for speed and performance
Tools to convert Caffe / NNEF / ONNX pre-trained neural net models to an OpenVX Graph
A Simple and Fast Rest API for productionization the ONNX models
一种基于 YOLOv8 的路口交通信号灯通行规则识别模型及算法
This repository shows an example of how to use the ONNX standard to interoperate between different frameworks. In this example, we train a model with PyTorch and make predictions with Tensorflow, ONNX Runtime, and Caffe2.
Easy to install image and video colorization using onnx converted deoldify model
Easy-to-use danbooru anime image classification model
Add a description, image, and links to the onnx-models topic page so that developers can more easily learn about it.
To associate your repository with the onnx-models topic, visit your repo's landing page and select "manage topics."