ShuffleNetV1

Introduction

ShuffleNet is a computationally efficient CNN model proposed by KuangShi Technology in 2017, which, like MobileNet and SqueezeNet, etc., is mainly intended to be applied to mobile. ShuffleNet uses two operations at its core: pointwise group convolution and channel shuffle, which greatly reduces the model computation while maintaining accuracy. ShuffleNet designs more efficient network structures to achieve smaller and faster models, instead of compressing or migrating a large trained model.

Figure 1. Architecture of ShuffleNetV1 [1]

Results

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

Model Context Top-1 (%) Top-5 (%) Params (M) Recipe Download
shufflenet_v1_g3_x0_5 D910x8-G 57.05 79.73 0.73 yaml weights
shufflenet_v1_g3_x1_0 D910x8-G 67.77 87.73 1.89 yaml weights
shufflenet_v1_g3_x1_5 D910x8-G 71.53 90.17 3.48 yaml weights
shufflenet_v1_g3_x2_0 D910x8-G 74.02 91.74 5.50 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

# distrubted training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/shufflenet_v1/shufflenet_v1_0.5_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/shufflenet_v1/shufflenet_v1_0.5_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/shufflenet_v1/shufflenet_v1_0.5_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] Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848-6856.