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README.md

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# 【AAAI'2023】Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
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# 🔥【AAAI'2023】Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/transferring-textual-knowledge-for-visual/action-classification-on-kinetics-400)](https://paperswithcode.com/sota/action-classification-on-kinetics-400?p=transferring-textual-knowledge-for-visual)
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/transferring-textual-knowledge-for-visual/action-recognition-in-videos-on-activitynet)](https://paperswithcode.com/sota/action-recognition-in-videos-on-activitynet?p=transferring-textual-knowledge-for-visual)
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/transferring-textual-knowledge-for-visual/action-recognition-in-videos-on-ucf101)](https://paperswithcode.com/sota/action-recognition-in-videos-on-ucf101?p=transferring-textual-knowledge-for-visual)
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## Updates
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- [ ] Models: The trained models & logs.
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## 📣 Updates
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- [x] **[Dec 22, 2022]** Models: The trained models & logs.
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- [x] **[Nov 30, 2022]** Config: All the configs (general/few-shot/zero-shot video recognition) on Kinetics-400 & 600, ActivityNet, UCF, and HMDB.
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- [x] **[Nov 30, 2022]** Code: Zero-shot Evaluation: Half-classes evaluation and Full-classes evaluation.
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- [x] **[Nov 29, 2022]** Code: Distributed training for InfoNCE and Compatible with CE.
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## Overview
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## 🌈 Overview
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![1](teaser.png)
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![2](text4vis.png)
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* [Model Zoo](#model-zoo)
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## Prerequisites
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## 📕 Prerequisites
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The code is built with following libraries:
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- [PyTorch](https://pytorch.org/) >= 1.8
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- Optional: decord (for on-the-fly video training)
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- Optional: torchnet (for mAP evaluation on ActivityNet)
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## Data Preparation
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## 📚 Data Preparation
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#### Video Loader
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**(Recommend)** To train all of our models, we extract videos into frames for fast reading. Please refer to [MVFNet](https://github.com/whwu95/MVFNet/blob/main/data_process/DATASETS.md) repo for the detaied guide of data processing.
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## Training
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## 🚀 Training
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This implementation supports Multi-GPU `DistributedDataParallel` training, which is faster and simpler than `DataParallel` used in [ActionCLIP](https://github.com/sallymmx/actionclip).
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- **Single Machine**: To train our model on Kinetics-400 with 8 GPUs in *Single Machine*, you can run:
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```
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## Testing
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## Testing
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We support single view validation and multi-view (4x3 views) validation.
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#### General/Few-shot Video Recognition
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## Model Zoo
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## 📱 Model Zoo
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Here we provide some off-the-shelf pre-trained checkpoints of our models in the followin tables.
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| Architecture |#Frame | Top-1 Acc.(%) | checkpoint | Train log| config|
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|:------------:|:-------------------:|:------------------:|:-----------------:|:--------------:|:--------------:|
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| ViT-B/32 | 8x3x4 | 80.0 | - | [log](exps/k400/ViT-B/32/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitb-32-f8.yaml) |
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| ViT-B/32 | 16x3x4 | 80.5 | - | [log](exps/k400/ViT-B/32/f16/log.txt) | [config](configs/k400/k400_train_rgb_vitb-32-f16.yaml) |
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| ViT-B/16 | 8x3x4 | 82.9 | - | [log](exps/k400/ViT-B/16/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitb-16-f8.yaml) |
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| ViT-B/16 | 16x3x4 | 83.6 | - | [log](exps/k400/ViT-B/16/f16/log.txt) | [config](configs/k400/k400_train_rgb_vitb-16-f16.yaml) |
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| ViT-L/14* | 8x3x4 | 86.4 | - | [log](exps/k400/ViT-L/14/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-f8.yaml) |
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| ViT-L/14-336 | 8x3x4 | 87.1 | - | [log](exps/k400/ViT-L/14-336px/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-336-f8.yaml) |
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| ViT-L/14-336 | 32x3x1 | 87.8 | - | [log](exps/k400/ViT-L/14-336px/f32/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-336-f32.yaml) |
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| ViT-B/32 | 8x3x4 | 80.0 | [Github](https://github.com/whwu95/Text4Vis/releases/download/v1/k400-vitb-32-f8.pt) | [log](exps/k400/ViT-B/32/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitb-32-f8.yaml) |
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| ViT-B/32 | 16x3x4 | 80.5 | [Github](https://github.com/whwu95/Text4Vis/releases/download/v1/k400-vitb-32-f16.pt) | [log](exps/k400/ViT-B/32/f16/log.txt) | [config](configs/k400/k400_train_rgb_vitb-32-f16.yaml) |
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| ViT-B/16 | 8x3x4 | 82.9 | [Github](https://github.com/whwu95/Text4Vis/releases/download/v1/k400-vitb-16-f8.pt) | [log](exps/k400/ViT-B/16/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitb-16-f8.yaml) |
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| ViT-B/16 | 16x3x4 | 83.6 | [Github](https://github.com/whwu95/Text4Vis/releases/download/v1/k400-vitb-16-f16.pt)| [log](exps/k400/ViT-B/16/f16/log.txt) | [config](configs/k400/k400_train_rgb_vitb-16-f16.yaml) |
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| ViT-L/14* | 8x3x4 | 86.4 | [GoogleDrive](https://drive.google.com/file/d/1tGfE6HDjTGZ7-y6XM7D6UJAx1Esj-q7u/view?usp=share_link) | [log](exps/k400/ViT-L/14/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-f8.yaml) |
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| ViT-L/14-336 | 8x3x4 | 87.1 | [BaiduCloud](https://pan.baidu.com/s/1aRyU08rdNjNZzGEFHaGLDw) pwd:uwl6 | [log](exps/k400/ViT-L/14-336px/f8/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-336-f8.yaml) |
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| ViT-L/14-336 | 32x3x1 | 87.8 | [GoogleDrive](https://drive.google.com/file/d/1NWl4J6YkwUt1vVwzCj85svz_tLfR72zk/view?usp=share_link) | [log](exps/k400/ViT-L/14-336px/f32/log.txt) | [config](configs/k400/k400_train_rgb_vitl-14-336-f32.yaml) |
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*Note: * indicates that this ViT-L model is used for the zero-shot evaluation on UCF, HMDB, ActivityNet and Kinetics-600.*
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#### ActivityNet
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| Architecture |#Frame | mAP (%) | checkpoint | Train log| config|
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| ViT-L/14 | 16x1x1 | 96.5 | - | [log](exps/anet/ViT-L/14/f16/log.txt) | [config](configs/anet/anet_k400_finetune.yaml) |
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| ViT-L/14-336 | 16x1x1 | 96.9 | - | [log](exps/anet/ViT-L/14-336px/f16/log.txt) | [config](configs/anet/anet_k400_finetune_336.yaml) |
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| ViT-L/14 | 16x1x1 | 96.5 | [BaiduCloud](https://pan.baidu.com/s/1Gum9cSlj7B1IHOgUZjUdUw) pwd:e9m7 | [log](exps/anet/ViT-L/14/f16/log.txt) | [config](configs/anet/anet_k400_finetune.yaml) |
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| ViT-L/14-336 | 16x1x1 | 96.9 | [BaiduCloud](https://pan.baidu.com/s/12rHqi9R1cFORpZEbqhvsfg) pwd:duq7 | [log](exps/anet/ViT-L/14-336px/f16/log.txt) | [config](configs/anet/anet_k400_finetune_336.yaml) |
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#### UCF-101
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| Architecture |#Frame | Top-1 Acc. (%) | checkpoint | Train log| config|
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|:------------:|:-------------------:|:------------------:|:-----------------:|:--------------:|:--------------:|
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| ViT-L/14 | 16x1x1 | 98.1 | - | [log](exps/ucf101/ViT-L/14/f16/log.txt) | [config](configs/ucf101/ucf_k400_finetune.yaml) |
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| ViT-L/14-336 | 16x1x1 | 98.2 | - | [log](exps/ucf101/ViT-L/14-336px/f16/log.txt) | [config](configs/ucf101/ucf_k400_finetune_336.yaml) |
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| ViT-L/14 | 16x1x1 | 98.1 | [BaiduCloud](https://pan.baidu.com/s/1jI6gKnzqeOLtJJyK9vBLhw) pwd:i93p | [log](exps/ucf101/ViT-L/14/f16/log.txt) | [config](configs/ucf101/ucf_k400_finetune.yaml) |
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<!-- | ViT-L/14-336 | 16x1x1 | 98.2 | - | [log](exps/ucf101/ViT-L/14-336px/f16/log.txt) | [config](configs/ucf101/ucf_k400_finetune_336.yaml) | -->
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| Architecture |#Frame | Top-1 Acc. (%) | checkpoint | Train log| config|
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| ViT-L/14 | 16x1x1 | 81.3 | - | [log](exps/hmdb51/ViT-L/14/f16/log.txt) | [config](configs/hmdb51/hmdb_k400_finetune.yaml) |
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| ViT-L/14 | 16x1x1 | 81.3 | [Baidu Cloud](https://pan.baidu.com/s/1aa_uBAc0Cauww8WZMWmqsQ) pwd:023f | [log](exps/hmdb51/ViT-L/14/f16/log.txt) | [config](configs/hmdb51/hmdb_k400_finetune.yaml) |
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## Bibtex
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## 📌 Bibtex
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If you find this repository useful, please star🌟 this repo and cite📑 our paper:
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```
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@article{wu2022transferring,
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title={Revisiting Classifier: Transferring Vision-Language Models for Video Recognition},
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author={Wu, Wenhao and Sun, Zhun and Ouyang, Wanli},
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booktitle={AAAI Conference on Artificial Intelligence (AAAI)},
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year={2022}
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year={2023}
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}
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```
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## Acknowledgement
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## 🎗️ Acknowledgement
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This repository is built based on [ActionCLIP](https://github.com/sallymmx/actionclip) and [CLIP](https://github.com/openai/CLIP). Sincere thanks to their wonderful works.
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## Contact
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## 👫 Contact
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For any question, please file an issue or contact [Wenhao Wu](https://whwu95.github.io/)
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clip/clip.py

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}
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def _download(url: str, root: str = os.path.expanduser("../clip-pretrain")):
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def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
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os.makedirs(root, exist_ok=True)
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filename = os.path.basename(url)
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