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-[x]**[Nov 29, 2022]** Code: Distributed training for InfoNCE and Compatible with CE.
@@ -29,7 +29,7 @@ This is the official implementation of the paper [Revisiting Classifier: Transfe
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## Overview
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## 🌈 Overview
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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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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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