memAE
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unofficial implementation of paper Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection
Hi! I wonder if I could train and test your model on pictures. My task is industrial defect detection.
I train the models with your codes on UCSD Ped2 dataset, but I cannot get the report results. 
Traceback (most recent call last): File "D:/python/pycharm/memAE-master/Train.py", line 81, in train_dataset = data_utils.DataLoader(train_folder, frame_trans, time_step=args.t_length - 1, num_pred=1) File "D:\python\pycharm\memAE-master\data\utils.py", line 22, in __init__ self.videos, video_string = setup(self.dir, self.videos) File...
in author's paper  in my results  and note: I used the pretrained model in ckpt(model-40.pt)
When I train the model with my custom data, the train memory sparse loss increases first and then decreases. could you tell me the reason?
How to visualize the experimental results, as shown in the figure below 
Thanks your work! Did your code work well on shanghaitech? or if you have the trained model about avenue or Shanghaitech datasets . and could you please send me the...
Hi, I have test you code and found you memory loss weight is 0, when I attempt to set thie weight to 0.0002, I found the att_weight tend to all...
--------------PyTorch VERSION: 1.7.0+cu101 ..............device cpu Traceback (most recent call last): File "/content/memAE-2/Train.py", line 76, in args.dataset_augment_test_type) File "/content/memAE-2/data/utils.py", line 148, in give_data_folder return train_folder, test_folder UnboundLocalError: local variable 'train_folder' referenced...
hello, thank you for your work. Can you share the pretrained model of UCSD Ped2?