Порождение подписей к изображениям. Классификация изображений. Прогнозирование временных рядов
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Updated
Jun 2, 2021 - Jupyter Notebook
Порождение подписей к изображениям. Классификация изображений. Прогнозирование временных рядов
Generates textual description of any given image. Use both Natural Language Processing (NLP) and Computer Vision to generate captions. The idea implemented is to replace the encoder (RNN layer) in an encoder-decoder architecture with a deep Convolutional Neural Network (CNN) trained to classify objects in images.
Deep Learning Photo Caption Generator Tensorflow 2.0
This repository trains image captioning model using CNN and Transformers.
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Image Captioner Model
Personalized Image Captioning with Transformer Models
BLIP-ImageCaption
image caption generator, dog breed classifier, stock forecasting 🤖🖼️ Порождение подписей на русском языке к изображениям (Python, Keras). Собрал нейросеть из двух частей – свёрточная и рекуррентная части. Получил датасет путём перевода на русский датасета Flickr 8k с помощью Yandex Translate API. Получил метрику BLEU равной 0.51.
Immodal: Storytellor who never lies! Image caption generator + story generator powered by Cohere API
Python-based solution for automatic image caption generation using a ResNet-50 CNN and RNN, featuring comprehensive data preprocessing, model training, and evaluation with BLEU score and Cosine Similarity metrics.
Image Captioning Using CLIP & GPT Models
deep learning model for generate caption and Analysis Sentiment
COL774 Machine Learning Spring-2019-2020 IIT Delhi. Instructor - Prof. Parag Singla
Image Captioning is a task where each image must be understood properly and are able generate suitable caption with proper grammatical structure. Here it is a hybrid system which uses multilayer CNN (Convolutional Neural Network) for generating keywords.
This project Implements a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate descriptive captions for input images.
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