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README

ShuffleNetV2_x2_0 (IGIE)

Model Description

ShuffleNetV2_x2_0 is a lightweight convolutional neural network introduced in the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" by Megvii (Face++). It is designed to achieve high performance with low computational cost, making it ideal for mobile and embedded devices.The x2_0 in its name indicates a width multiplier of 2.0, meaning the model has twice as many channels compared to the baseline ShuffleNetV2_x1_0. It employs Channel Shuffle to enable efficient information exchange between grouped convolutions, addressing the limitations of group convolutions. The core building block, the ShuffleNetV2 block, features a split-merge design and channel shuffle mechanism, ensuring both high efficiency and accuracy.

Supported Environments

Iluvatar GPU IXUCA SDK
MR-V100 4.2.0

Model Preparation

Prepare Resources

Pretrained model: https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth

Dataset: https://www.image-net.org/download.php to download the validation dataset.

Install Dependencies

pip3 install -r requirements.txt

Model Conversion

python3 export.py --weight shufflenetv2_x2_0-8be3c8ee.pth --output shufflenetv2_x2_0.onnx

Model Inference

export DATASETS_DIR=/Path/to/imagenet_val/

FP16

# Accuracy
bash scripts/infer_shufflenetv2_x2_0_fp16_accuracy.sh
# Performance
bash scripts/infer_shufflenetv2_x2_0_fp16_performance.sh

Model Results

Model BatchSize Precision FPS Top-1(%) Top-5(%)
ShuffleNetV2_x2_0 32 FP16 5439.098 76.176 92.860
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