YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection

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非常感谢大家的star！YOWOv2是我业余时间做的一个尝试，是对上一代的YOWO的一次致敬。YOWO曾是我很喜欢的一个工作，但如今我已不再继续研究这一方向，因此，无法再回答大家的各种问题，对此，我深感抱歉，还望大家能够谅解。YOWOv2是一个完全开放的项目，不包含任何的license，因此，请尽管做任何你想做的改进或优化，无需经过我的同意。只要这个项目能对世界进步带去哪怕是微乎其微的促进和贡献，我也足够开心了~如果您觉得我们这个工作还行，不妨引用我们挂在Arxiv上的论文链接吧（在README的最下方）。

Thank you very much for everyone's star. YOWOv2 is an attempt I made in my spare time. It is a tribute to YOWO because I used to like YOWO very much. However, I am no longer deeply involved in the field of spatiotemporal motion detection, so I am not in a position to answer some of everyone's issues. For this, I am deeply sorry. YOWOv2 is a completely open spatiotemporal action detection project. I have not added any license to the project, so please feel free to do whatever you want without my consent. As long as my work can make even a small contribution to the progress of the world, I will be very happy. If you think our work is useful, you could cite our article posted on Arxiv (at the bottom of README).

Overview of YOWOv2

Requirements

• We recommend you to use Anaconda to create a conda environment:

  conda create -n yowo python=3.6

• Then, activate the environment:

  conda activate yowo

• Requirements:

  pip install -r requirements.txt 

Visualization

Dataset

UCF101-24:

You can download UCF24 from the following links:

• Google drive

Link: https://drive.google.com/file/d/1Dwh90pRi7uGkH5qLRjQIFiEmMJrAog5J/view?usp=sharing

• BaiduYun Disk

Link: https://pan.baidu.com/s/11GZvbV0oAzBhNDVKXsVGKg

Password: hmu6

AVA

You can use instructions from here to prepare AVA dataset.

Experiment

• UCF101-24

All FLOPs are measured with a video clip with 16 or 32 frames (224×224). The FPS is measured with batch size 1 on a 3090 GPU from the model inference to the NMS operation.

Qualitative results on UCF101-24

• AVA v2.2

Qualitative results on AVA

Train YOWOv2

• UCF101-24

For example:

python train.py --cuda -d ucf24 --root path/to/dataset -v yowo_v2_nano --num_workers 4 --eval_epoch 1 --max_epoch 8 --lr_epoch 2 3 4 5 -lr 0.0001 -ldr 0.5 -bs 8 -accu 16 -K 16

or you can just run the script:

sh train_ucf.sh

• AVA

  python train.py --cuda -d ava_v2.2 --root path/to/dataset -v yowo_v2_nano --num_workers 4 --eval_epoch 1 --max_epoch 10 --lr_epoch 3 4 5 6 -lr 0.0001 -ldr 0.5 -bs 8 -accu 16 -K 16 --eval

or you can just run the script:

sh train_ava.sh

If you have multiple GPUs, you can launch DDP to train the YOWOv2, for example:

python train.py --cuda -dist -d ava_v2.2 --root path/to/dataset -v yowo_v2_nano --num_workers 4 --eval_epoch 1 --max_epoch 10 --lr_epoch 3 4 5 6 -lr 0.0001 -ldr 0.5 -bs 8 -accu 16 -K 16 --eval

However, I have not multiple GPUs, so I am not sure if there are any bugs, or if the given performance can be reproduced using DDP.

Test YOWOv2

• UCF101-24 For example:

python test.py --cuda -d ucf24 -v yowo_v2_nano --weight path/to/weight -size 224 --show

• AVA For example:

python test.py --cuda -d ava_v2.2 -v yowo_v2_nano --weight path/to/weight -size 224 --show

Test YOWOv2 on AVA video

For example:

python test_video_ava.py --cuda -d ava_v2.2 -v yowo_v2_nano --weight path/to/weight --video path/to/video --show

Note that you can set path/to/video to other videos in your local device, not AVA videos.

Evaluate YOWOv2

• UCF101-24 For example:

# Frame mAP
python eval.py \
        --cuda \
        -d ucf24 \
        -v yowo_v2_nano \
        -bs 16 \
        -size 224 \
        --weight path/to/weight \
        --cal_frame_mAP \

# Video mAP
python eval.py \
        --cuda \
        -d ucf24 \
        -v yowo_v2_nano \
        -bs 16 \
        -size 224 \
        --weight path/to/weight \
        --cal_video_mAP \

• AVA

Run the following command to calculate frame mAP@0.5 IoU:

python eval.py \
        --cuda \
        -d ava_v2.2 \
        -v yowo_v2_nano \
        -bs 16 \
        --weight path/to/weight

Demo

# run demo
python demo.py --cuda -d ucf24 -v yowo_v2_nano -size 224 --weight path/to/weight --video path/to/video --show
                      -d ava_v2.2

Qualitative results in real scenarios

References

If you are using our code, please consider citing our paper.

@article{yang2023yowov2,
  title={YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection},
  author={Yang, Jianhua and Kun, Dai},
  journal={arXiv preprint arXiv:2302.06848},
  year={2023}
}