Awesome

DeepMIL Pytorch Version

Unofficial implemention of "Real-world Anomaly Detection in Surveillance Videos" CVPR2018

The feature extractor is here: https://github.com/DavideA/c3d-pytorch

we have released I3D features of UCF-Crime, which can be downloaded from: https://stuxidianeducn-my.sharepoint.com/:f:/g/personal/pengwu_stu_xidian_edu_cn/EvYcZ5rQZClGs_no2g-B0jcB4ynsonVQIreHIojNnUmPyA?e=xNrGxc

where we oversample each video frame with the “10-crop” augment, “10-crop” means cropping images into the center, four corners, and their mirrored counterparts. _0.npy is the center, _1~ _4.npy is the corners, and _5 ~ _9 is the mirrored counterparts.

To achieve better performance, we suggest use I3D features rather than C3D features.

• How to train

  1. download or extract the features.
  2. use make_list.py in the list folder to generate the training and test list.
  3. change the parameters in option.py
  4. run main.py

• How to test

  run infer.py and the model is in the ckpt folder.

We also released a audio-visual violence dataset named XD-Violence (ECCV2020), the project website is here: https://roc-ng.github.io/XD-Violence/ . We have released the I3D and VGGish features of our dataset as well as the code.

In order to make training process faster, we suggest use the following code to replace original code in train.py [Line 34]

model.train()
n_iter = iter(nloader)
a_iter = iter(aloader)
for i in range(30):  # 800/batch_size
    ninput = next(n_iter)
    ainput = next(a_iter)

Thanks for your attention!