Awesome

Knowledge Distillation from A Stronger Teacher (DIST)

Official implementation of paper "Knowledge Distillation from A Stronger Teacher" (DIST), NeurIPS 2022.
By Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu.

:fire: DIST: a simple and effective KD method.

Updates

December 27, 2022: Update CIFAR-100 distillation code and logs.
September 20, 2022: Release code for semantic segmentation task.
September 15, 2022: DIST was accepted by NeurIPS 2022!
May 30, 2022: Code for object detection is available.
May 27, 2022: Code for ImageNet classification is available.

Getting started

Clone training code

git clone https://github.com/hunto/DIST_KD.git --recurse-submodules
cd DIST_KD

The loss function of DIST is in classification/lib/models/losses/dist_kd.py.

classification: prepare your environment and datasets following the README.md in classification.
object detection: coming soon.
semantic segmentation: coming soon.

Reproducing our results

ImageNet

cd classification
sh tools/dist_train.sh 8 ${CONFIG} ${MODEL} --teacher-model ${T_MODEL} --experiment ${EXP_NAME}

Baseline settings (R34-R101 and R50-MBV1):
```
CONFIG=configs/strategies/distill/resnet_dist.yaml
```
Student Teacher DIST MODEL T_MODEL Log Ckpt
ResNet-18 (69.76) ResNet-34 (73.31) 72.07 tv_resnet18 tv_resnet34 log ckpt
MobileNet V1 (70.13) ResNet-50 (76.16) 73.24 mobilenet_v1 tv_resnet50 log ckpt

Student	Teacher	DIST	MODEL	T_MODEL	Log	Ckpt
ResNet-18 (69.76)	ResNet-34 (73.31)	72.07	`tv_resnet18`	`tv_resnet34`	log	ckpt
MobileNet V1 (70.13)	ResNet-50 (76.16)	73.24	`mobilenet_v1`	`tv_resnet50`	log	ckpt

Stronger teachers (R18 and R34 students with various ResNet teachers):

Student	Teacher	KD (T=4)	DIST
ResNet-18 (69.76)	ResNet-34 (73.31)	71.21	72.07
ResNet-18 (69.76)	ResNet-50 (76.13)	71.35	72.12
ResNet-18 (69.76)	ResNet-101 (77.37)	71.09	72.08
ResNet-18 (69.76)	ResNet-152 (78.31)	71.12	72.24
ResNet-34 (73.31)	ResNet-50 (76.13)	74.73	75.06
ResNet-34 (73.31)	ResNet-101 (77.37)	74.89	75.36
ResNet-34 (73.31)	ResNet-152 (78.31)	74.87	75.42

Stronger training strategies:

CONFIG=configs/strategies/distill/dist_b2.yaml

ResNet-50-SB: stronger ResNet-50 trained by TIMM (ResNet strikes back) .

Student	Teacher	KD (T=4)	DIST	MODEL	T_MODEL	Log
ResNet-18 (73.4)	ResNet-50-SB (80.1)	72.6	74.5	`tv_resnet18`	`timm_resnet50`	log
ResNet-34 (76.8)	ResNet-50-SB (80.1)	77.2	77.8	`tv_resnet34`	`timm_resnet50`	log
MobileNet V2 (73.6)	ResNet-50-SB (80.1)	71.7	74.4	`tv_mobilenet_v2`	`timm_resnet50`	log
EfficientNet-B0 (78.0)	ResNet-50-SB (80.1)	77.4	78.6	<details>`timm_tf_efficientnet_b0`	`timm_resnet50`</details>	log
ResNet-50 (78.5)	Swin-L (86.3)	80.0	80.2	`tv_resnet50`	<details>`timm_swin_large_patch4_window7_224`</details>	log ckpt
Swin-T (81.3)	Swin-L (86.3)	81.5	82.3	-	-	log

Swin-L student: We implement our DIST on the official code of Swin-Transformer.

CIFAR-100

Download and extract the teacher checkpoints to your disk, then specify the path of the corresponding checkpoint pth file using --teacher-ckpt:

cd classification
sh tools/dist_train.sh 1 configs/strategies/distill/dist_cifar.yaml ${MODEL} --teacher-model ${T_MODEL} --experiment ${EXP_NAME} --teacher-ckpt ${CKPT}

NOTE: For MobileNetV2, ShuffleNetV1, and ShuffleNetV2, lr and warmup-lr should be 0.01:

sh tools/dist_train.sh 1 configs/strategies/distill/dist_cifar.yaml ${MODEL} --teacher-model ${T_MODEL} --experiment ${EXP_NAME} --teacher-ckpt ${CKPT} --lr 0.01 --warmup-lr 0.01

Student	Teacher	DIST	MODEL	T_MODEL	Log
WRN-40-1 (71.98)	WRN-40-2 (75.61)	74.43±0.24	`cifar_wrn_40_1`	`cifar_wrn_40_2`	log
ResNet-20 (69.06)	ResNet-56 (72.34)	71.75±0.30	`cifar_resnet20`	`cifar_resnet56`	log
ResNet-8x4 (72.50)	ResNet-32x4 (79.42)	76.31±0.19	`cifar_resnet8x4`	`cifar_resnet32x4`	log
MobileNetV2 (64.60)	ResNet-50 (79.34)	68.66±0.23	`cifar_mobile_half`	`cifar_ResNet50`	log
ShuffleNetV1 (70.50)	ResNet-32x4 (79.42)	76.34±0.18	`cifar_ShuffleV1`	`cifar_resnet32x4`	log
ShuffleNetV2 (71.82)	ResNet-32x4 (79.42)	77.35±0.25	`cifar_ShuffleV2`	`cifar_resnet32x4`	log

COCO Detection

The training code is in MasKD/mmrazor. An example to train cascade_mask_rcnn_x101-fpn_r50:

sh tools/mmdet/dist_train_mmdet.sh configs/distill/dist/dist_cascade_mask_rcnn_x101-fpn_x50_coco.py 8 work_dirs/dist_cmr_x101-fpn_x50

Student	Teacher	DIST	DIST+mimic	Config	Log
Faster RCNN-R50 (38.4)	Cascade Mask RCNN-X101 (45.6)	40.4	41.8	[DIST] [DIST+Mimic]	[DIST] [DIST+Mimic]
RetinaNet-R50 (37.4)	RetinaNet-X101 (41.0)	39.8	40.1	[DIST] [DIST+Mimic]	[DIST] [DIST+Mimic]

Cityscapes Segmentation

Detailed instructions of reproducing our results are in segmentation folder (README).

Student	Teacher	DIST	Log
DeepLabV3-R18 (74.21)	DeepLabV3-R101 (78.07)	77.10	log
PSPNet-R18 (72.55)	DeepLabV3-R101 (78.07)	76.31	log

License

This project is released under the Apache 2.0 license.

Citation

@article{huang2022knowledge,
  title={Knowledge Distillation from A Stronger Teacher},
  author={Huang, Tao and You, Shan and Wang, Fei and Qian, Chen and Xu, Chang},
  journal={arXiv preprint arXiv:2205.10536},
  year={2022}
}