Just make some notes on SSKD for personal study

SSKD

This repo is the implementation of paper Knowledge Distillation Meets Self-Supervision (ECCV 2020).

Prerequisite

This repo is tested with Ubuntu 16.04.5, Python 3.7, PyTorch 1.5.0, CUDA 10.2. Make sure to install pytorch, torchvision, tensorboardX, numpy before using this repo.

Running

Teacher Training

An example of teacher training is:

python teacher.py --arch wrn_40_2 --lr 0.05 --gpu-id 0

where you can specify the architecture via flag --arch

You can also download all the pre-trained teacher models here. If you want to run student.py directly, you have to re-organise the directory. For instance, when you download vgg13.pth, you have to make a directory for it, say teacher_vgg13, and then make a new directory ckpt inside teacher_vgg13. Move the vgg13.pth into teacher_vgg13/ckpt and rename it as best.pth. If you want a simpler way to use pre-trained model, you can edit the code in student.py (line 90).

Student Training

An example of student training is:

python student.py --t-path ./experiments/teacher_wrn_40_2_seed0/ --s-arch wrn_16_2 --lr 0.05 --gpu-id 0

python student.py --t-path teacher_wrn_40_2/ --s-arch wrn_16_2 --lr 0.05 --gpu-id 7

The meanings of flags are:

--t-path: teacher's checkpoint path. Automatically search the checkpoint containing 'best' keyword in its name.

--s-arch: student's architecture.

All the commands can be found in command.sh

Results (Top-1 Acc) on CIFAR100

Similar-Architecture

Teacher Student	wrn40-2 wrn16-2	wrn40-2 wrn40-1	resnet56 resnet20	resnet32x4 resnet8x4	vgg13 vgg8
Teacher Student	76.46 73.64	76.46 72.24	73.44 69.63	79.63 72.51	75.38 70.68
KD	74.92	73.54	70.66	73.33	72.98
FitNet	75.75	74.12	71.60	74.31	73.54
AT	75.28	74.45	71.78	74.26	73.62
SP	75.34	73.15	71.48	74.74	73.44
VID	74.79	74.20	71.71	74.82	73.96
RKD	75.40	73.87	71.48	74.47	73.72
PKT	76.01	74.40	71.44	74.17	73.37
AB	68.89	75.06	71.49	74.45	74.27
FT	75.15	74.37	71.52	75.02	73.42
CRD	76.04	75.52	71.68	75.90	74.06
SSKD	76.04	76.13	71.49	76.20	75.33

Cross-Architecture

Teacher Student	vgg13 MobieleNetV2	ResNet50 MobileNetV2	ResNet50 vgg8	resnet32x4 ShuffleV1	resnet32x4 ShuffleV2	wrn40-2 ShuffleV1
Teacher Student	75.38 65.79	79.10 65.79	79.10 70.68	79.63 70.77	79.63 73.12	76.46 70.77
KD	67.37	67.35	73.81	74.07	74.45	74.83
FitNet	68.58	68.54	73.84	74.82	75.11	75.55
AT	69.34	69.28	73.45	74.76	75.30	75.61
SP	66.89	68.99	73.86	73.80	75.15	75.56
VID	66.91	68.88	73.75	74.28	75.78	75.36
RKD	68.50	68.46	73.73	74.20	75.74	75.45
PKT	67.89	68.44	73.53	74.06	75.18	75.51
AB	68.86	69.32	74.20	76.24	75.66	76.58
FT	69.19	69.01	73.58	74.31	74.95	75.18
CRD	68.49	70.32	74.42	75.46	75.72	75.96
SSKD	71.53	72.57	75.76	78.44	78.61	77.40

Citation

If you find this repo useful for your research, please consider citing the paper

@inproceedings{xu2020knowledge,
    title={Knowledge Distillation Meets Self-Supervision},
    author={Xu, Guodong and Liu, Ziwei and Li, Xiaoxiao and Loy, Chen Change},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2020},
}

Acknowledgement

The implementation of models is borrowed from CRD