In this setting, we have a large number of normal data, and a few of abnormal data with temporal annotation, where we know which frame is normal or abnormal.

We perform k-fold cross validation on avenue and shanghaitech dataset. In avenue dataset, we re-annotate the labels of each frames, and the re-annotated file is avenue.json. We set k=10, in avenue dataset and in shanghaiTech dataset, we set k=5. The following script is an example to train the model on avenue dataset with the kth = 1 folder. We change kth to other folders.

python train_scripts/train_temporal_annotation.py --dataset  avenue    \
         --prednet  cyclegan_convlstm    \
         --batch    2                    \
         --num_his  4                    \
         --label_level  temporal         \
         --k_folds  10                   \
         --kth      1                    \
         --gpu      0                    \
         --iters    80000   --output_dir  ./outputs

Inference and evaluation.

After we train the model, we run the inference and evaluate all the checkpoints. If there a more than 2 GPUs, you can immediately run the inference scripts after run the training scripts, because the inference script is always listening the directory of the checkpoints, once there is a new checkpoint, it will evaluate it immediately. Here we use gpu 0 for training, and gpu 1 for testing.

python inference.py  --dataset  avenue    \
         --prednet  cyclegan_convlstm     \
         --num_his  4                     \
         --label_level  temporal          \
         --k_folds  10                    \
         --kth      1                     \
         --gpu      1                     \
         --interpolation   --output_dir  ./outputs