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imprinted-weights

This is an unofficial pytorch implementation of Low-Shot Learning with Imprinted Weights.

Requirements

  • Python 3.5+
  • PyTorch 0.4.1
  • torchvision
  • pandas (0.23.4)
  • progress
  • matplotlib
  • numpy

Major Difference

Paper: InceptionV1 + RMSProp

This implementation: ResNet-50 + SGD

Preparation

Download CUB_200_2011 Dataset.

Unzip and locate it in this directory.

The whole directory should be look like this:

imprinted-weights
│   README.md
│   pretrain.py
│   models.py
│   loader.py
│   imprint.py
│   imprint_ft.py
│   alljoint.py
|
└───utils
│   
└───CUB_200_2011
    │   images.txt
    │   image_class_labels.txt
    │   train_test_split.txt
    │
    └───images
        │   001.Black_footed_Albatross
        │   002.Laysan_Albatross
        │   ...

Usage

Pretrain models

Train the model on the first 100 classes of CUB_200_2011.

python pretrain.py

Trained models will be saved at pretrain_checkpoint.

Imprint weights

Use N novel exemplar from the training split to imprint weights.

python imprint.py --model pretrain_checkpoint/model_best.pth.tar --num-sample N

Trained models will be saved at imprint_checkpoint.

For more details and parameters, please refer to --help option.

All w/o FT results of Table 1 and Table 2 in the paper can be reproduced by this script.

Imprint weights + FT

Apply fine-tuning to the imprinting model.

python imprint_ft.py --model pretrain_checkpoint/model_best.pth.tar --num-sample N

Trained models will be saved at imprint_ft_checkpoint.

All w/ FT results of Table 1 and Table 2 in the paper can be reproduced by this script.

All class joint

Train model for base and novel classes jointly without a separate low-shot learning phase.

python alljoint.py --num-sample N

Trained models will be saved at alljoint_checkpoint.

Results

200-way top-1 accuracy for novel-class examples in CUB-200-2011

w/o FT

n = 1 2 5 10 20
Rand-noFT (paper) 0.17 0.17 0.17 0.17 0.17
Imprinting (paper) 21.26 28.69 39.52 45.77 49.32
Imprinting + Aug (paper) 21.40 30.03 39.35 46.35 49.80
Rand-noFT 0.00 0.00 0.00 0.00 0.00
Imprinting 28.77 37.61 50.20 56.31 60.58
Imprinting + Aug 28.81 38.02 49.90 56.18 60.44

w/ FT

n = 1 2 5 10 20
Rand + FT (paper) 5.25 13.41 34.95 54.33 65.60
Imprinting + FT (paper) 18.67 30.17 46.08 59.39 68.77
AllClassJoint (paper) 3.89 10.82 33.00 50.24 64.88
Rand + FT 2.39 13.00 36.11 56.83 69.90
Imprinting + FT 26.14 34.81 54.40 63.41 73.41
AllClassJoint 7.34 18.09 45.67 60.58 73.41

200-way top-1 accuracy measured across examples in all classes of CUB-200-2011

w/o FT

n = 1 2 5 10 20
Rand-noFT (paper) 37.36 37.36 37.36 37.36 37.36
Imprinting (paper) 44.75 48.21 52.95 55.99 57.47
Imprinting + Aug (paper) 44.60 48.48 52.78 56.51 57.84
Rand-noFT 41.39 41.39 41.39 41.39 41.39
Imprinting 53.50 57.35 62.65 65.38 67.09
Imprinting + Aug 53.40 57.47 62.56 65.21 67.26

w/ FT

n = 1 2 5 10 20
Rand + FT (paper) 39.26 43.36 53.69 63.17 68.75
Imprinting + FT (paper) 45.81 50.41 59.15 64.65 68.73
AllClassJoint (paper) 38.02 41.89 52.24 61.11 68.31
Rand + FT 41.27 46.41 57.71 67.79 74.72
Imprinting + FT 53.02 57.82 67.21 71.73 76.25
AllClassJoint 42.73 48.43 62.20 69.95 75.89

References

  • [1]: H. Qi, M. Brown and D. Lowe. "Low-Shot Learning with Imprinted Weights", in CVPR, 2018.