Pytorch implementation of the paper "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles" by Mehdi Noroozi GitHub

Partially tested Performances Coming Soon

• Tested with Python 2.7
• Pytorch v0.3
• Tensorflow is used for logging. Remove the Logger all scripts if tensorflow is missing

Two DataLoader are provided:

• ImageLoader: per each iteration it loads data in image format (jpg,png ,...)
  • Dataset/JigsawImageLoader.py uses PyTorch DataLoader and iterator
  • Dataset/ImageDataLoader.py custom implementation.

The default loader is JigsawImageLoader.py. ImageDataLoader.py is slightly faster when using single core.

The images can be preprocessed using produce_small_data.py which resize the image to 256, keeping the aspect ratio, and crops a patch of size 255x255 in the center.

Fill the path information in run_jigsaw_training.sh. IMAGENET_FOLD needs to point to the folder containing ILSVRC2012_img_train.

./run_jigsaw_training.sh [GPU_ID]

or call the python script

python JigsawTrain.py [*path_to_imagenet*] --checkpoint [*path_checkpoints_and_logs*] --gpu [*GPU_ID*] --batch [*batch_size*]

By default the network uses 1000 permutations with maximum hamming distance selected using select_permutations.py.

To change the file name loaded for the permutations, open the file JigsawLoader.py and change the permutation file in the method retrive_permutations

• The input of the network should be 64x64, but I need to resize to 75x75, otherwise the output of conv5 is 2x2 instead of 3x3 like the official architecture
• Jigsaw trained using the approach of the paper: SGD, LRN layers, 70 epochs
• Implemented shortcuts: spatial jittering, normalize each patch indipendently, color jittering, 30% black&white image
• The LRN layer crushes with a PyTorch version older than 0.3

• TensorboardX
• LMDB DataLoader