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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)

Knowledge Distillation (KD)

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