MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021)

Overview

We release the code of the MVFNet (Multi-View Fusion Network). The core code to implement the Multi-View Fusion Module is codes/models/modules/MVF.py.

[Mar 24, 2021] We has released the code of MVFNet.

[Dec 20, 2020] MVFNet has been accepted by AAAI 2021.

• Prerequisites
• Data Preparation
• Model Zoo
• Testing
• Training

Prerequisites

All dependencies can be installed using pip:

python -m pip install -r requirements.txt

Our experiments run on Python 3.7 and PyTorch 1.5. Other versions should work but are not tested.

Download Pretrained Models

• Download ImageNet pre-trained models

cd pretrained
sh download_imgnet.sh

• Download K400 pre-trained models

Please refer to Model Zoo.

Data Preparation

Please refer to DATASETS.md for data preparation.

Model Zoo

Testing

• For 3 crops, 10 clips, the processing of testing

# Dataset: Kinetics-400
# Architecture: R50_8x8 [email protected]=76.0%
bash scripts/dist_test_recognizer.sh configs/MVFNet/K400/mvf_kinetics400_2d_rgb_r50_dense.py ckpt_path 8 --fcn_testing

Training

This implementation supports multi-gpu, DistributedDataParallel training, which is faster and simpler.

• For example, to train MVFNet-ResNet50 on Kinetics400 with 8 gpus, you can run:

bash scripts/dist_train_recognizer.sh configs/MVFNet/K400/mvf_kinetics400_2d_rgb_r50_dense.py 8

• We also provide the script to train MVFNet on Kinetics400 with multiple machines (e.g., 2 machines and 16 GPUs).

# For first machine, --master_addr is the ip of your first machine
bash scripts/dist_train_multinode_1.sh configs/MVFNet/K400/mvf_kinetics400_2d_rgb_r50_dense.py 8

# For second machine, --master_addr is still the ip of your first machine
bash scripts/dist_train_multinode_2.sh configs/MVFNet/K400/mvf_kinetics400_2d_rgb_r50_dense.py 8

Acknowledgements

We especially thank the contributors of the mmaction codebase for providing helpful code.

License

This repository is released under the Apache-2.0. license as found in the LICENSE file.

Citation

If you think our work is useful, please feel free to cite our paper 😆 :

@inproceedings{wu2020MVFNet,
  author    = {Wu, Wenhao and He, Dongliang and Lin, Tianwei and Li, Fu and Gan, Chuang and Ding, Errui},
  title     = {MVFNet: Multi-View Fusion Network for Efficient Video Recognition},
  booktitle = {AAAI},
  year      = {2021}
}

Contact

For any question, please file an issue or contact

Wenhao Wu: [email protected]