opengvlab/efficient-video-recognition

Frozen CLIP models are Efficient Video Learners

This is the official implementation of the paper Frozen CLIP models are Efficient Video Learners

@article{lin2022frozen,
  title={Frozen CLIP Models are Efficient Video Learners},
  author={Lin, Ziyi and Geng, Shijie and Zhang, Renrui and Gao, Peng and de Melo, Gerard and Wang, Xiaogang and Dai, Jifeng and Qiao, Yu and Li, Hongsheng},
  journal={arXiv preprint arXiv:2208.03550},
  year={2022}
}

Introduction

The overall architecture of the EVL framework includes a trainable Transformer decoder, trainable local temporal modules and a pretrained, fixed image backbone (CLIP is used for instance).

Using a fixed backbone significantly saves training time, and we managed to train a ViT-B/16 with 8 frames for 50 epochs in 60 GPU-hours (NVIDIA V100).

Despite with a small training computation and memory consumption, EVL models achieves high performance on Kinetics-400. A comparison with state-of-the-art methods are as follows

Installation

We tested the released code with the following conda environment

conda create -n pt1.9.0cu11.1_official -c pytorch -c conda-forge pytorch=1.9.0=py3.9_cuda11.1_cudnn8.0.5_0 cudatoolkit torchvision av

Data Preparation

We expect that --train_list_path and --val_list_path command line arguments to be a data list file of the following format

<path_1> <label_1>
<path_2> <label_2>
...
<path_n> <label_n>

where <path_i> points to a video file, and <label_i> is an integer between 0 and num_classes - 1. --num_classes should also be specified in the command line argument.

Additionally, <path_i> might be a relative path when --data_root is specified, and the actual path will be relative to the path passed as --data_root.

The class mappings in the open-source weights are provided at Kinetics-400 class mappings

Backbone Preparation

CLIP weights need to be downloaded from CLIP official repo and passed to the --backbone_path command line argument.

Script Usage

Training and evaliation scripts are provided in the scripts folder. Scripts should be ready to run once the environment is setup and --backbone_path, --train_list_path and --val_list_path are replaced with your own paths.

For other command line arguments please see the help message for usage.

Kinetics-400 Main Results

This is a re-implementation for open-source use. We are still re-running some models, and their scripts, weights and logs will be released later. In the following table we report the re-run accuracy, which may be slightly different from the original paper (typically +/-0.1%)

Backbone	Decoder Layers	#frames x stride	top-1	top-5	Script	Model	Log
ViT-B/16	4	8 x 16	82.8	95.8	script	google drive	google drive
ViT-B/16	4	16 x 16	83.7	96.2	script	google drive	google drive
ViT-B/16	4	32 x 8	84.3	96.6	script	google drive	google drive
ViT-L/14	4	8 x 16	86.3	97.2	script	google drive	google drive
ViT-L/14	4	16 x 16	86.9	97.4	script	google drive	google drive
ViT-L/14	4	32 x 8	87.7	97.6	script	google drive	google drive
ViT-L/14 (336px)	4	32 x 8	87.7	97.8

Data Loading Speed

As the training process is fast, video frames are consumed at a very high rate. For easier installation, the current version uses PyTorch-builtin data loaders. They are not very efficient and can become a bottleneck when using ViT-B as backbones. We provide a --dummy_dataset option to bypass actual video decoding for training speed measurement. The model accuracy should not be affected. Our internal data loader is pure C++-based and does not bottleneck training by much on a machine with 2x Xeon Gold 6148 CPUs and 4x V100 GPUs.

Acknowledgements

The data loader code is modified from PySlowFast. Thanks for their awesome work!