python测试开发库 中文版(持续更新)及书籍下载。公众号：pythontesting

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

deep learning for image processing including classification and object-detection etc.

This repository implements the Breakfast Action Dataset in Pytorch and aims to achieve a high human action recognition using the C3D model

Datasets, Transforms and Models specific to Computer Vision

Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

This is an official implementation for "Video Swin Transformers".

Video Swin Transformer - PyTorch

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

Implementation of the paper Video Action Transformer Network

PyTorch implementation of a collections of scalable Video Transformer Benchmarks.

Appearance-and-Relation Networks

The implementation of Sequential VLAD in Pytorch

Transformer based human action recognition in videos

UCF101 Video Classification Assignment for CRCV REU 2021

Implementation of ViViT: A Video Vision Transformer

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Code that accompanies my blog post outlining five video classification methods in Keras and TensorFlow

PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"

Exploration of different solutions to action recognition in video, using neural networks implemented in PyTorch.

Inflated i3d network with inception backbone, weights transfered from tensorflow

Video Transformer Network

PyTorch implemented C3D, R3D, R2Plus1D models for video activity recognition.

A large scale video database for violence detection, which has 2,000 video clips containing violent or non-violent behaviours.

Tiny Kinetics-400 for test

Code and models for our CVPR'19 paper "Representation Flow for Action Recognition"