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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]

Results

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

Model Context Top-1 (%) Top-5 (%) Params (M) Recipe Weight
volo_d1 D910x8-G 82.59 95.99 27 yaml weights
volo_d2 D910x8-G 82.95 96.13 59 yaml weights
volo_d3 D910x8-G 83.38 96.28 87 yaml weights
volo_d4 D910x8-G 82.5 95.86 193 yaml weights

Notes

  • Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
  • Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.

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 GPU/Ascend devices
mpirun -n 8 python train.py --config configs/volo/volo_d1_ascend.yaml --data_dir /path/to/imagenet

If the script is executed by the root user, the --allow-run-as-root parameter must be added to mpirun

Similarly, you can train the model on multiple GPU devices with the above mpirun command.

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

Deployment

To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.

References

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