MIST_VAD
Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"
Updates
[May 28th] Testing / Training codes have been released. The codes are cleaned out from the original ones without full verification.
There maybe any unexpected bugs. I will improve it later if I have time.
Requirements
- python>=3.6
- apex
- pytorch=1.5.0+cu101
- torchvision=0.6.0+cu101
- tensorboardX
- h5py
- opencv
- scikit-learn
- yacs
Testing
Pretrained models have been uploaded on OneDrive.
The h5py file for ShanghaiTech and its corresponing annotations are uploaded on [BaiduYun] with multiple sub-files, you can open/unzip it with WinRAR
BaiduYun link, code：kym5
To test the pretrained checkpoints, you are recommended to read Testing_Guidelines.md for more details.
Training
We have released the training codes for ShanghaiTech and UCF-Crimes. For convenience to repeat our experiments, we presents the pseudo labels files in data/ dir.
The details of training are listed in Training_Guidelines.md.
Reference
If you feel the codes help, please cite our paper.
Recommended Citation Form:
Jia-Chang Feng, Fa-Ting Hong and Wei-Shi Zheng. “MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 2021.