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# Installation, Training and Evaluation Instructions for Image Classification
We provide installation, training and evaluation instructions for image classification here.
## Installation Instructions
- Clone this repo:
```bash
git clone https://github.com/megvii-research/RevCol.git
cd RevCol
```
- Create a conda virtual environment and activate it:
```bash
conda create --name revcol python=3.7 -y
conda activate revcol
```
- Install `CUDA>=11.3` with `cudnn>=8` following
the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- Install `PyTorch>=1.11.0` and `torchvision>=0.12.0` with `CUDA>=11.3`:
```bash
conda install pytorch=1.11.0 torchvision=0.12.0 torchaudio=0.11.0 cudatoolkit=11.3 -c pytorch
```
- Install `timm==0.5.4`:
```bash
pip install timm==0.5.4
```
- Install other requirements:
```bash
pip install -r requirements.txt
```
## Data preparation
We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
load data:
- For standard imagenet-1k dataset, the file structure should look like:
```bash
path-to-imagenet-1k
├── train
│ ├── class1
│ │ ├── img1.jpeg
│ │ ├── img2.jpeg
│ │ └── ...
│ ├── class2
│ │ ├── img3.jpeg
│ │ └── ...
│ └── ...
└── val
├── class1
│ ├── img4.jpeg
│ ├── img5.jpeg
│ └── ...
├── class2
│ ├── img6.jpeg
│ └── ...
└── ...
```
- For ImageNet-22K dataset, the file structure should look like:
```bash
path-to-imagenet-22k
├── class1
│ ├── img1.jpeg
│ ├── img2.jpeg
│ └── ...
├── class2
│ ├── img3.jpeg
│ └── ...
└── ...
```
- As imagenet-22k has no val set, one way is to use imagenet-1k val set as the evaluation for imagenet 22k dataset. Please remember to map the imagenet-1k label to imagenet-22k.
```bash
path-to-imagenet-22k-custom-eval-set
├── class1
│ ├── img1.jpeg
│ ├── img2.jpeg
│ └── ...
├── class2
│ ├── img3.jpeg
│ └── ...
└── ...
```
## Evaluation
To evaluate a pre-trained `RevCol` on ImageNet validation set, run:
```bash
torchrun --nproc_per_node=<num-of-gpus-to-use> --master_port=23456 main.py --cfg <config-file.yaml> --resume <checkpoint_path> --data-path <imagenet-path> --eval
```
For example, to evaluate the `RevCol-T` with a single GPU:
```bash
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_tiny_1k.yaml --resume path_to_your_model.pth --eval
```
## Training from scratch on ImageNet-1K
To train a `RevCol` on ImageNet from scratch, run:
```bash
torchrun --nproc_per_node=<num-of-gpus-to-use> --master_port=23456 main.py \
--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
```
**Notes**:
For example, to train `RevCol` with 8 GPU on a single node for 300 epochs, run:
`RevCol-T`:
```bash
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_tiny_1k.yaml --batch-size 128 --data-path <imagenet-path>
```
`RevCol-S`:
```bash
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_small_1k.yaml --batch-size 128 --data-path <imagenet-path>
```
`RevCol-B`:
```bash
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_base_1k.yaml --batch-size 128 --data-path <imagenet-path>
```
## Pre-training on ImageNet-22K
For example, to pre-train a `RevCol-B` model on ImageNet-22K:
```bash
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_large_22k_pretrain.yaml --batch-size 128 --data-path <imagenet-22k-path> --opt DATA.EVAL_DATA_PATH <imagenet-22k-custom-eval-path>
```
## Fine-tuning from a ImageNet-22K(21K) pre-trained model
For example, to fine-tune a `RevCol-B` model pre-trained on ImageNet-22K(21K):
```bashs
torchrun --nproc_per_node=8 --master_port=23456 main.py --cfg configs/revcol_base_1k_384_finetune.yaml --batch-size 64 --data-path <imagenet-22k-path> --finetune revcol_base_22k_pretrained.pth
```
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