Skip to content

Latest commit

 

History

History

VOLO

README.md

VOLO: Vision Outlooker for Visual Recognition, arxiv

PaddlePaddle training/validation code and pretrained models for VOLO.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

VOLO Model Overview

Update

  • Update (2022-04-11): Code is updated.
  • Update (2021-09-27): More weights are uploaded.
  • Update (2021-08-11): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
volo_d1_224 84.12 96.78 26.6M 6.6G 224 1.0 bicubic google/baidu
volo_d1_384 85.24 97.21 26.6M 19.5G 384 1.0 bicubic google/baidu
volo_d2_224 85.11 97.19 58.6M 13.7G 224 1.0 bicubic google/baidu
volo_d2_384 86.04 97.57 58.6M 40.7G 384 1.0 bicubic google/baidu
volo_d3_224 85.41 97.26 86.2M 19.8G 224 1.0 bicubic google/baidu
volo_d3_448 86.50 97.71 86.2M 80.3G 448 1.0 bicubic google/baidu
volo_d4_224 85.89 97.54 192.8M 42.9G 224 1.0 bicubic google/baidu
volo_d4_448 86.70 97.85 192.8M 172.5G 448 1.0 bicubic google/baidu
volo_d5_224 86.08 97.58 295.3M 70.6G 224 1.0 bicubic google/baidu
volo_d5_448 86.92 97.88 295.3M 283.8G 448 1.0 bicubic google/baidu
volo_d5_512 87.05 97.97 295.3M 371.3G 512 1.15 bicubic google/baidu

*The results are evaluated on ImageNet2012 validation set.

Data Preparation

ImageNet2012 dataset is used in the following file structure:

│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
  • train_list.txt: list of relative paths and labels of training images. You can download it from: google/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume weight file is downloaded in ./botnet50.pdparams, to use the botnet50 model in python:

from config import get_config
from botnet import build_botnet50 as build_model
# config files in ./configs/
config = get_config('./configs/botnet50.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./botnet50.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

To evaluate model performance on ImageNet2012, run the following script using command line:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/botnet50.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./botnet50.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

NOTE: Now training is not fully developed, full code is coming soon

To train the model on ImageNet2012, run the following script using command line:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/botnet50.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp

Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.

Reference

@article{yuan2021volo,
  title={Volo: Vision outlooker for visual recognition},
  author={Yuan, Li and Hou, Qibin and Jiang, Zihang and Feng, Jiashi and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2106.13112},
  year={2021}
}