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https://github.com/hunto/image_classification_sota
Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.
https://github.com/hunto/image_classification_sota
cifar image-classification imagenet kd nas pruning pytorch rep transformer vit
Last synced: 3 months ago
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Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.
- Host: GitHub
- URL: https://github.com/hunto/image_classification_sota
- Owner: hunto
- License: apache-2.0
- Created: 2022-03-11T08:24:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-20T04:26:48.000Z (8 months ago)
- Last Synced: 2024-04-17T00:19:03.588Z (7 months ago)
- Topics: cifar, image-classification, imagenet, kd, nas, pruning, pytorch, rep, transformer, vit
- Language: Python
- Homepage:
- Size: 208 KB
- Stars: 63
- Watchers: 3
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://github.com/hunto/DIST_KD)).
### 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]](https://arxiv.org/abs/2103.13425) [[original repo]](https://github.com/DingXiaoH/DiverseBranchBlock)
* DyRep (CVPR 2022) [[README]](https://github.com/hunto/DyRep)
### Knowledge Distillation (KD)
* KD [[paper]](https://arxiv.org/abs/1503.02531)
* DIST [[README]](https://github.com/hunto/DIST_KD) [[paper]](https://arxiv.org/abs/2205.10536)
## 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
```shell
python tools/train.py -c ${CONFIG} --model ${MODEL} [optional arguments]
```
* Training with multiple GPUs
```shell
sh tools/dist_train.sh ${GPU_NUM} ${CONFIG} ${MODEL} [optional arguments]
```
* For slurm users
```shell
sh tools/slurm_train.sh ${PARTITION} ${GPU_NUM} ${CONFIG} ${MODEL} [optional arguments]
```
**Examples**
* Train ResNet-50 on ImageNet
```shell
sh tools/dist_train.sh 8 configs/strategies/resnet/resnet.yaml resnet50 --experiment imagenet_res50
```
* Train MobileNetV2 on ImageNet
```shell
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
```shell
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](https://github.com/hunto/DyRep): Bootstrapping Training with Dynamic Re-parameterization
* [NeurIPS 2022] [DIST](https://github.com/hunto/DIST_KD): Knowledge Distillation from A Stronger Teacher
* [LightViT](https://github.com/hunto/LightViT): Towards Light-Weight Convolution-Free Vision Transformers