This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

Overview

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv]

Overview

ActionCLIP

Content

Prerequisites

The code is built with following libraries:

  • PyTorch >= 1.8
  • wandb
  • RandAugment
  • pprint
  • tqdm
  • dotmap
  • yaml
  • csv

For video data pre-processing, you may need ffmpeg.

More detail information about libraries see INSTALL.md.

Data Preparation

We need to first extract videos into frames for fast reading. Please refer to TSN repo for the detailed guide of data pre-processing. We have successfully trained on Kinetics, UCF101, HMDB51, Charades.

Updates

  • We now support single crop validation(including zero-shot) on Kinetics-400, UCF101 and HMDB51. The pretrained models see MODEL_ZOO.md for more information.
  • we now support the model-training on Kinetics-400, UCF101 and HMDB51 on 8, 16 and 32 frames. The model-training configs see configs/README.md for more information.
  • We now support the model-training on your own datasets. The detail information see configs/README.md.

Pretrained Models

Training video models is computationally expensive. Here we provide some of the pretrained models. We provide a large set of trained models in the ActionCLIP MODEL_ZOO.md.

Kinetics-400

We experiment ActionCLIP with different backbones(we choose Transf as our final visual prompt since it obtains the best results) and input frames configurations on k400. Here is a list of pre-trained models that we provide (see Table 6 of the paper).

model n-frame top1 Acc(single-crop) top5 Acc(single-crop) checkpoint
ViT-B/32 8 78.36% 94.25% link pwd:8hg2
ViT-B/16 8 81.09% 95.49% link
ViT-B/16 16 81.68% 95.87% link
ViT-B/16 32 82.32% 96.20% link pwd:v7nn

HMDB51 && UCF101

On HMDB51 and UCF101 datasets, the accuracy(k400 pretrained) is reported under the accurate setting.

HMDB51

model n-frame top1 Acc(single-crop) checkpoint
ViT-B/16 32 76.2% link

UCF101

model n-frame top1 Acc(single-crop) checkpoint
ViT-B/16 32 97.1% link

Testing

To test the downloaded pretrained models on Kinetics or HMDB51 or UCF101, you can run scripts/run_test.sh. For example:

# test
bash scripts/run_test.sh  ./configs/k400/k400_ft_tem.yaml

Zero-shot

We provide several examples to do zero-shot validation on kinetics-400, UCF101 and HMDB51.

  • To do zero-shot validation on Kinetics from CLIP pretrained models, you can run:
# zero-shot
bash scripts/run_test.sh  ./configs/k400/k400_ft_zero_shot.yaml
  • To do zero-shot validation on UCF101 and HMDB51 from Kinetics pretrained models, you need first prepare the k400 pretrained model and then you can run:
# zero-shot
bash scripts/run_test.sh  ./configs/hmdb51/hmdb_ft_zero_shot.yaml

Training

We provided several examples to train ActionCLIP with this repo:

  • To train on Kinetics from CLIP pretrained models, you can run:
# train 
bash scripts/run_train.sh  ./configs/k400/k400_ft_tem_test.yaml
  • To train on HMDB51 from Kinetics400 pretrained models, you can run:
# train 
bash scripts/run_train.sh  ./configs/hmdb51/hmdb_ft.yaml
  • To train on UCF101 from Kinetics400 pretrained models, you can run:
# train 
bash scripts/run_train.sh  ./configs/ucf101/ucf_ft.yaml

More training details, you can find in configs/README.md

Contributors

ActionCLIP is written and maintained by Mengmeng Wang and Jiazheng Xing.

Citing ActionCLIP

If you find ActionClip useful in your research, please use the following BibTex entry for citation.

@inproceedings{wang2022ActionCLIP,
  title={ActionCLIP: A New Paradigm for Video Action Recognition},
  author={Mengmeng Wang, Jiazheng Xing and Yong Liu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2021}
} 

Acknowledgments

Our code is based on CLIP and STM.

🎁 3,000,000+ Unsplash images made available for research and machine learning

The Unsplash Dataset The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of

Unsplash 2k Jan 03, 2023
Catbird is an open source paraphrase generation toolkit based on PyTorch.

Catbird is an open source paraphrase generation toolkit based on PyTorch. Quick Start Requirements and Installation The project is based on PyTorch 1.

Afonso Salgado de Sousa 5 Dec 15, 2022
MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system Getting started To start working on this assignment, you should

2 Aug 06, 2022
YOLOX + ROS(1, 2) object detection package

YOLOX + ROS(1, 2) object detection package

Ar-Ray 158 Dec 21, 2022
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

DeepVecFont This is the official Pytorch implementation of the paper: Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts

Yizhi Wang 146 Dec 18, 2022
Neural implicit reconstruction experiments for the Vector Neuron paper

Neural Implicit Reconstruction with Vector Neurons This repository contains code for the neural implicit reconstruction experiments in the paper Vecto

Congyue Deng 35 Jan 02, 2023
Label Hallucination for Few-Shot Classification

Label Hallucination for Few-Shot Classification This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classific

Yiren Jian 13 Nov 13, 2022
Nb workflows - A workflow platform which allows you to run parameterized notebooks programmatically

NB Workflows Description If SQL is a lingua franca for querying data, Jupyter sh

Xavier Petit 6 Aug 18, 2022
Python Multi-Agent Reinforcement Learning framework

- Please pay attention to the version of SC2 you are using for your experiments. - Performance is *not* always comparable between versions. - The re

whirl 1.3k Jan 05, 2023
Official release of MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer axriv: http://arxiv.org/abs/2112.13513

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis This is the official page of the MSHT with its experimental script and records. We de

Tianyi Zhang 53 Dec 27, 2022
A library for performing coverage guided fuzzing of neural networks

TensorFuzz: Coverage Guided Fuzzing for Neural Networks This repository contains a library for performing coverage guided fuzzing of neural networks,

Brain Research 195 Dec 28, 2022
The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:

9 Nov 14, 2022
Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements

Alfred-Restore-Iterm-Arrangement This alfred workflow will list avaliable iTerm2

7 May 10, 2022
Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)

Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) authors: Boris Knyazev, Michal Drozdzal, Graham Taylor, Adriana Romero-Soriano Overv

Facebook Research 462 Jan 03, 2023
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
Collaborative forensic timeline analysis

Timesketch Table of Contents About Timesketch Getting started Community Contributing About Timesketch Timesketch is an open-source tool for collaborat

Google 2.1k Dec 28, 2022
masscan + nmap + Finger

说明 个人根据使用习惯修改masnmap而来的一个小工具。调用masscan做全端口扫描,再调用nmap做服务识别,最后调用Finger做Web指纹识别。工具使用场景适合风险探测排查、众测等。 使用方法 安装依赖 pip3 install -r requirements.txt -i https:/

Ryan 3 Mar 25, 2022
StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN

StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN This is the PyTorch implementation of StyleGAN of All Trades: Image Manipulati

360 Dec 28, 2022
pytorch implementation of fast-neural-style

fast-neural-style 🌇 🚀 NOTICE: This codebase is no longer maintained, please use the codebase from pytorch examples repository available at pytorch/e

Abhishek Kadian 405 Dec 15, 2022