This repository contains an op-for-op PyTorch reimplementation of ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design .

• ShuffleNetV2-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • How Test and Train
    • Test
    • Train model
    • Resume train model
  • Result
  • Contributing
  • Credit
    • ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

• Google Driver
• Baidu Driver

Contains MNIST, CIFAR10&CIFAR100, TinyImageNet_200, MiniImageNet_1K, ImageNet_1K, Caltech101&Caltech256 and more etc.

• Google Driver
• Baidu Driver

Please refer to README.md in the data directory for the method of making a dataset.

Both training and testing only need to modify the config.py file.

• line 29: model_arch_name change to shufflenet_v2_x1_0.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to test.
• line 89: model_weights_path change to ./results/pretrained_models/ShuffleNetV2_x1_0-ImageNet_1K-25000dee.pth.tar.

python3 test.py

• line 29: model_arch_name change to shufflenet_v2_x1_0.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to train.
• line 50: pretrained_model_weights_path change to ./results/pretrained_models/ShuffleNetV2_x1_0-ImageNet_1K-25000dee.pth.tar.

python3 train.py

• line 29: model_arch_name change to shufflenet_v2_x1_0.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to train.
• line 53: resume change to ./samples/shufflenet_v2_x1_0-ImageNet_1K/epoch_xxx.pth.tar.

python3 train.py

Source of original paper results: https://arxiv.org/pdf/1807.11164v1.pdf)

In the following table, the top-x error value in () indicates the result of the project, and - indicates no test.

# Download `ShuffleNetV2_x1_0-ImageNet_1K-25000dee.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py 

Input:

Output:

Build `shufflenet_v2_x1_0` model successfully.
Load `shufflenet_v2_x1_0` model weights `/ShuffleNetV2-PyTorch/results/pretrained_models/ShuffleNetV2_x1_0-ImageNet_1K-25000dee.pth.tar` successfully.
tench, Tinca tinca                                                          (84.78%)
barracouta, snoek                                                           (2.71%)
gar, garfish, garpike, billfish, Lepisosteus osseus                         (0.43%)
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch    (0.28%)
American lobster, Northern lobster, Maine lobster, Homarus americanus       (0.25%)

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian

Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.

[Paper]

@inproceedings{ma2018shufflenet, 
            title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},  
            author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},  
            booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},  
            pages={116--131}, 
            year={2018} 
}