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SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The
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 is a PyTorch self-supervised learning framework designed to train models that learn visual representations from video. It implements a joint-embedding predictive architecture that extracts spatio-temporal features by predicting missing regions of a signal within a latent representation space rather than reconstructing raw pixels. The project includes a latent space visualization tool that uses a conditional diffusion model to decode feature-space predictions back into pixels. This allows for the verification of learned representations by transforming abstract predictions into interpretab
YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on
An efficient video loader for deep learning with smart shuffling that's super easy to digest
The main features of dmlc/decord are: Computer Vision, Video Representation Learning.
Open-source alternatives to dmlc/decord include: open-mmlab/mmaction2 — mmaction2 is a PyTorch video understanding toolbox designed for training and evaluating deep learning models. It… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset… facebookresearch/jepa — This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from… ailab-cvc/yolo-world — YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images… alankbi/detecto — Build fully-functioning computer vision models with PyTorch. ajabri/videowalk.