volo

VOLO

VOLO: Vision Outlooker for Visual Recognition

Introduction

Vision Outlooker (VOLO), a novel outlook attention, presents a simple and general architecture. Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens, which is shown to be critically beneficial to recognition performance but largely ignored by the self-attention. Five versions different from model scaling are introduced based on the proposed VOLO: VOLO-D1 with 27M parameters to VOLO-D5 with 296M. Experiments show that the best one, VOLO-D5, achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark, without using any extra training data.

Figure 1. Illustration of outlook attention. [1]

Requirements

mindspore	ascend driver	firmware	cann toolkit/kernel
2.5.0	24.1.0	7.5.0.3.220	8.0.0.beta1

Quick Start

Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Distributed Training

It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run

# distributed training on multiple Ascend devices
msrun --bind_core=True --worker_num 8 python train.py --config configs/volo/volo_d1_ascend.yaml --data_dir /path/to/imagenet

For detailed illustration of all hyper-parameters, please refer to config.py.

Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.

• Standalone Training

If you want to train or finetune the model on a smaller dataset without distributed training, please run:

# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/volo/volo_d1_ascend.yaml --data_dir /path/to/dataset --distribute False

Validation

To validate the accuracy of the trained model, you can use validate.py and parse the checkpoint path with --ckpt_path.

python validate.py -c configs/volo/volo_d1_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Performance

Our reproduced model performance on ImageNet-1K is reported as follows.

Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode.

model name	params(M)	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	acc@top1	acc@top5	recipe	weight
volo_d1	26.63	8	128	224x224	O2	368s	230.05	4451.21	82.97	96.21	yaml	weights

Notes

• top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.

References

[1] Yuan L , Hou Q , Jiang Z , et al. VOLO: Vision Outlooker for Visual Recognition[J]. . arXiv preprint arXiv: 2106.13112, 2021.