DistillationLab

untitledunmastered1998/DistillationLab

Requirements

experiment environment

python3.8.12
pytorch1.10.1

1. datasets

dataset	#train samples	#test samples	#classes	resolution
CIFAR100	50000	10000	100	low
MNIST	60000	10000	10	low
vggface2	2763078	548208	9131	low
ImageNet	1281167	50000	1000	high
ImageNet_subset	12610	5000	100	high
ImageNet32	1281167	50000	1000	low
ImageNet32_reduced	384631	15000	300	low
Tiny-ImageNet	100000	10000	200	low
Cars	8144	8041	196	high
flowers102	2040	6149	102	high
stanford_dogs	12601	8519	120	high
aircrafts	6667	3333	100	high

2. models

Available teacher and student networks including:
'resnet32', 'ResNet18', 'ResNet34', 'ResNet50', 'ResNet101', 'ResNet152',
'mobilenet_v2',
'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0',
'squeezenet1_0', 'squeezenet1_1'

networks	parameters
resnet32
ResNet18
ResNet34
ResNet50
ResNet101
ResNet152
mobilenet_v2
shufflenet_v2_x0_5
shufflenet_v2_x1_0
shufflenet_v2_x1_5
shufflenet_v2_x2_0
squeezenet1_0
squeezenet1_1

3. distillation methods

① knowledge distillation [Distilling the Knowledge in a Neural Network] (https://arxiv.org/abs/1503.02531)
② L2
③ FitNets [FitNets: Hints for Thin Deep Nets] (https://arxiv.org/abs/1412.6550)
④ PKT [Learning Deep Representations with Probabilistic Knowledge Transfer] ECCV2018 (https://arxiv.org/abs/1803.10837)
⑤ RKD [Relational Knowledge Distillation] CVPR 2019(https://arxiv.org/abs/1904.05068)

4. setup

Baseline performance follows standard image classification training procedures.

5. training skills

tricks	performance
baseline
+xavier init / kaiming init
+pretrained weights
+no bias decay
+label smoothing
+random erasing
+linear scaling learning rate
+cutout
+dropout
+cosine learning rate decay
+warm up stage
+mixup
+Zero γ
data augmentation
Learning Rate Schedule

6.experiments

experiments on CIFAR100

same student, different teacher networks

teacher	ResNet18	ResNet34	ResNet50	ResNet101	ResNet152
student	mobilenet_v2
t_baseline
s_baseline
KD
FitNets
RKD
PKT
L2
AT
overhaul

different student networks

teacher	ResNet18	ResNet34	ResNet50	ResNet101	ResNet152
student	mobilenet_v2	shufflenet_v1	squeezenet_v0	shufflenet_v2	WRN-16-2
t_baseline
s_baseline
KD
FitNets
RKD
PKT
L2
AT
overhaul

same architecture

teacher	ResNet18	ResNet34	ResNet50	ResNet101	ResNet152
student	resnet8×4	resnet32	resnet18	resnet34	resnet50
t_baseline
s_baseline
KD
FitNets
RKD
PKT
L2
AT
overhaul

cross-model transfer

datasets	student	KD	AT	L2	FitNets	CRD	RKD	PKT	teacher
CIFAR100→STL-10
CIFAR100→Tiny-ImageNet