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Mutual-Channel Loss for Fine-Grained Image Classification

Reimplementation of MCLoss on CUB_200_2011 dataset.

The code of mcloss is integrated into a class.

Requirements:

  • python 3
  • PyTorch 1.3.0
  • torchvision

Training:

  • Extract files to CUB_200_2011, run gen_train_test.py to get the label files, which are used by dataload.py, or modify the Dataset in train.py .
  • Train: python train.py , parameters are set as default.

Results:

  • The experiment is conduct with 2 RTX 2080Ti GPUs, and the batchsize is set to 32.
  • Trained from scratch:
    • Init_lr: 0.1 for all
    • lr_scheduler: MultiStepLR (total-300, milestones-[150, 225], lr_gamma-0.1)
    • The random seed is set for reproducibility.
Model cnums cgroups p alpha img_size feat_dim seed Acc@1
VGG16 [3] [200] 0.5 1.5 224->224 600*7*7 None 64.88
VGG16 [3] [200] 0.5 2.0 224->224 600*7*7 None 66.66
VGG16 [3] [200] 0.5 1.5 224->224 600*7*7 8 66.17
VGG16 [3] [200] 0.5 2.0 224->224 600*7*7 8 66.03
  • Using Imagenet pretrained model
    • Init_lr: 0.005 for conv layers, 0.05 for dense layers
    • lr_scheduler: StepLR (total-150, lr_step-30, lr_gamma-0.8)
Model cnums cgroups p alpha img_size feat_dim Acc@1
ResNet50 [10, 11] [152, 48] 0.4 0.0005 600->448 2048 86.93

PS: the results maybe influenced by random initial, using batch size 64 can be closer to the results of the paper.

Reference: