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This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a spatiotemporal architecture that analyzes both spatial frames and temporal motion to classify human activities within video clips. The system includes a distributed model training framework to accelerate learning across multiple compute nodes. It supports the deployment and fine-tuning of pre-trained model weights, allowing the adaptation of existing networks to specific new datasets. The codebase covers the full pipeline for spatiotemporal learning, including video dataset p
Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision architectures and training pipelines. It serves as a deep learning research toolkit and a model zoo containing state-of-the-art pre-trained weights for image and video analysis. The project includes a specialized human pose estimation library and a model compression toolkit. These tools allow for the pruning and quantization of deep learning models to increase inference speed and facilitate deployment on constrained edge hardware. The library covers a broad range of vision capabili
This project is an educational resource and comprehensive guide for implementing and deploying deep learning models using the PyTorch framework. It provides a structured learning curriculum consisting of tutorials and notebooks that cover neural network architectures, data pipelines, and model optimization across multiple AI domains. The curriculum includes practical implementation guides for building convolutional networks, transformers, and recurrent models. It specifically focuses on workflows for computer vision, including image classification, object detection, and segmentation, as well
mmaction2 is a PyTorch video understanding toolbox designed for training and evaluating deep learning models. It serves as a framework for action recognition, temporal localization, and spatio-temporal action detection, providing specialized tools for both pixel-based video analysis and skeleton-based action recognition. The project distinguishes itself through a modular architecture featuring registry-based component discovery and hierarchical, config-driven model assembly. It supports multi-modal feature fusion, integrating RGB frames, optical flow, and audio, and includes capabilities for
This project is a deep learning computer vision library designed for video action recognition. It provides a framework for training and evaluating neural networks that identify and categorize human activities within recorded footage by processing temporal sequences of frames.
The main features of jfzhang95/pytorch-video-recognition are: Action Recognition, Action Recognition Training, Video Content Analyzers, Video Sequence Architectures, Convolutional Layers, Deep Learning Training Toolsets, Computer Vision, PyTorch Tensor Operations.
Projects with overlapping indexed features include: kenshohara/3d-resnets-pytorch — This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a… dmlc/gluon-cv — Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision… datawhalechina/thorough-pytorch — This project is an educational resource and comprehensive guide for implementing and deploying deep learning models… open-mmlab/mmaction2 — mmaction2 is a PyTorch video understanding toolbox designed for training and evaluating deep learning models. It… fastai/course-v3 — This repository is a comprehensive educational program and deep learning framework designed to teach practical deep… udacity/deep-learning-v2-pytorch — This project is a collection of PyTorch deep learning courseware consisting of practical projects and programming…