kenshohara/3D-ResNets-PyTorch
3D ResNets PyTorch
Acest proiect este o implementare PyTorch a rețelelor reziduale 3D concepute pentru recunoașterea acțiunilor video. Oferă o arhitectură spatiotemporală care analizează atât cadrele spațiale, cât și mișcarea temporală pentru a clasifica activitățile umane în clipurile video.
Sistemul include un framework distribuit de antrenare a modelelor pentru a accelera învățarea pe mai multe noduri de calcul. Suportă implementarea și fine-tuning-ul ponderilor modelelor pre-antrenate, permițând adaptarea rețelelor existente la seturi de date noi specifice.
Baza de cod acoperă întregul pipeline pentru învățarea spatiotemporală, inclusiv instrumente de preprocesare a seturilor de date video pentru convertirea fișierelor brute în secvențe de imagini, capabilități de inferență a acțiunilor și metrici pentru calcularea acurateței recunoașterii.
Features
- Action Recognition - Classifies human activities and behaviors within video sequences using deep 3D residual networks.
- Action Recognition Training - Provides a framework for training neural networks to learn and recognize complex motion features in large-scale video datasets.
- 3D - Implements 3D residual networks specifically designed for classifying human actions in video sequences.
- 3D Convolutional Networks - Implements 3D convolutional networks to process spatiotemporal video data for action recognition.
- Spatiotemporal - Captures both appearance and motion patterns by analyzing video frames as single volumetric data blocks.
- Video Action Inference - Provides video action inference capabilities to predict activity class probabilities from trained models.
- Residual Connection Implementations - Employs residual connection implementations to mitigate vanishing gradients in deep spatiotemporal networks.
- Action Recognition Models - Implements an action recognition model based on 3D residual networks for identifying human activities in video.
- Data Parallelism - Provides data parallelism to split large video datasets across multiple compute nodes for synchronized training.
- Video Sequence Preprocessing - Provides video sequence preprocessing utilities to transform raw video into training-ready image frames.
- Distributed Video Model Training - Implements distributed video model training to scale the processing of extensive datasets across multiple GPU nodes.
- Pre-trained Weight Loading - Implements pre-trained weight loading for the immediate deployment of existing model checkpoints.
- Distributed Training - Ships a distributed training system for scaling spatiotemporal network learning across multiple compute nodes.
- Classification Fine-Tuning - Supports classification fine-tuning to adapt pre-trained weights to specific action recognition datasets.
- Vision Dataset Preparation - Provides tools for converting raw video files into formatted image sequences and annotations for training.
- Model Fine-Tuning - Provides mechanisms for optimizing pre-trained 3D ResNet weights on task-specific video datasets.
- Vision Model Fine-Tuning - Supports adapting pre-trained 3D ResNet weights to specific new datasets through specialized fine-tuning of the classification layers.
- Computer Vision Models - Action recognition using 3D residual networks.
- Computer Vision Research - Spatiotemporal 3D CNNs for video action recognition.
- Model Implementations - 3D ResNets for action recognition.
- Video Representation Learning - Provides 3D ResNet architectures for spatiotemporal feature learning.
- Facial, Action and Pose Recognition - Listed in the “Facial, Action and Pose Recognition” section of the The Incredible Pytorch awesome list.
