VOLO

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.

VOLO Model Overview

Update

• 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(xaim)
volo_d1_384	85.24	97.21	26.6M	19.5G	384	1.0	bicubic	google/baidu(rr7p)
volo_d2_224	85.11	97.19	58.6M	13.7G	224	1.0	bicubic	google/baidu(d82f)
volo_d2_384	86.04	97.57	58.6M	40.7G	384	1.0	bicubic	google/baidu(9cf3)
volo_d3_224	85.41	97.26	86.2M	19.8G	224	1.0	bicubic	google/baidu(a5a4)
volo_d3_448	86.50	97.71	86.2M	80.3G	448	1.0	bicubic	google/baidu(uudu)
volo_d4_224	85.89	97.54	192.8M	42.9G	224	1.0	bicubic	google/baidu(vcf2)
volo_d4_448	86.70	97.85	192.8M	172.5G	448	1.0	bicubic	google/baidu(nd4n)
volo_d5_224	86.08	97.58	295.3M	70.6G	224	1.0	bicubic	google/baidu(ymdg)
volo_d5_448	86.92	97.88	295.3M	283.8G	448	1.0	bicubic	google/baidu(qfcc)
volo_d5_512	87.05	97.97	295.3M	371.3G	512	1.15	bicubic	google/baidu(353h)

*The results are evaluated on ImageNet2012 validation set.

Notebooks

We provide a few notebooks in aistudio to help you get started:

*(coming soon)*

Requirements

• Python>=3.6
• yaml>=0.2.5
• PaddlePaddle>=2.1.0
• yacs>=0.1.8

Data

ImageNet2012 dataset is used in the following folder structure:

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

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 the downloaded weight file is stored in ./volo_d5_224.pdparams, to use the volo_d5_224 model in python:

from config import get_config
from volo import build_volo as build_model
# config files in ./configs/
config = get_config('./configs/volo_d5_224.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./volo_d5_224.pdparams')
model.set_dict(model_state_dict)

Evaluation

To evaluate VOLO model performance on ImageNet2012 with a single GPU, run the following script using command line:

sh run_eval.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
    -cfg=./configs/volo_d5_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/val \
    -eval \
    -pretrained=/path/to/pretrained/model/volo_d5_224  # .pdparams is NOT needed

Run evaluation using multi-GPUs:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg=./configs/volo_d5_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/val \
    -eval \
    -pretrained=/path/to/pretrained/model/volo_d5_224  # .pdparams is NOT needed

Training

To train the VOLO model on ImageNet2012 with single GPU, run the following script using command line:

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
  -cfg=./configs/volo_d5_224.yaml \
  -dataset=imagenet2012 \
  -batch_size=32 \
  -data_path=/path/to/dataset/imagenet/train

Run evaluation using multi-GPUs:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg=./configs/volo_d5_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/train

Visualization Attention Map

(coming soon)

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}
}