Awesome

Image Classification SOTA

Image Classification SOTA is an image classification toolbox based on PyTorch.

Updates

May 27, 2022

• Add knowledge distillation methods (KD and DIST).

March 24, 2022

• Support training strategies in DeiT (ViT).

March 11, 2022

• Release training code.

Supported Algorithms

Structural Re-parameterization (Rep)

• DBB (CVPR 2021) [paper] [original repo]
• DyRep (CVPR 2022) [README]

Knowledge Distillation (KD)

• KD [paper]
• DIST [README] [paper]

Requirements

torch>=1.0.1
torchvision

Getting Started

Prepare datasets

It is recommended to symlink the dataset root to image_classification_sota/data. Then the file structure should be like

image_classification_sota
├── lib
├── tools
├── configs
├── data
│   ├── imagenet
│   │   ├── meta
│   │   ├── train
│   │   ├── val
│   ├── cifar
│   │   ├── cifar-10-batches-py
│   │   ├── cifar-100-python

Training configurations

• Strategies: The training strategies are configured using yaml file or arguments. Examples are in configs/strategies directory.

Train a model

• Training with a single GPU

  python tools/train.py -c ${CONFIG} --model ${MODEL} [optional arguments]
  

• Training with multiple GPUs

  sh tools/dist_train.sh ${GPU_NUM} ${CONFIG} ${MODEL} [optional arguments]
  

• For slurm users

  sh tools/slurm_train.sh ${PARTITION} ${GPU_NUM} ${CONFIG} ${MODEL} [optional arguments]
  

Examples

• Train ResNet-50 on ImageNet

  sh tools/dist_train.sh 8 configs/strategies/resnet/resnet.yaml resnet50 --experiment imagenet_res50
  

• Train MobileNetV2 on ImageNet

  sh tools/dist_train.sh 8 configs/strategies/MBV2/mbv2.yaml nas_model --model-config configs/models/MobileNetV2/MobileNetV2.yaml --experiment imagenet_mbv2
  

• Train VGG-16 on CIFAR-10

  sh tools/dist_train.sh 1 configs/strategies/CIFAR/cifar.yaml nas_model --model-config configs/models/VGG/vgg16_cifar10.yaml --experiment cifar10_vgg16
  

Projects based on Image Classification SOTA

• [CVPR 2022] DyRep: Bootstrapping Training with Dynamic Re-parameterization
• [NeurIPS 2022] DIST: Knowledge Distillation from A Stronger Teacher
• LightViT: Towards Light-Weight Convolution-Free Vision Transformers