shufflenet_v2

ShuffleNetV2

Use cases

Computationally efficient CNN architecture designed specifically for mobile devices with very limited computing power.

Description

ShuffleNetV2 is an architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification.

Model

Model	Download	Checksum	Download (with sample test data)	ONNX version	Opset version	Top-1 error	Top-5 error
ShuffleNetV2	9.2MB	MD5	8.7MB	1.6	10	30.64	11.68

Inference

This script converts the model from PyTorch to ONNX and uses ONNX Runtime for inference.

Input to model

Input to the model are 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224.

Preprocessing steps

All pre-trained models expect input images normalized in the same way. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].

input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)

Create a mini-batch as expected by the model.

input_batch = input_tensor.unsqueeze(0)

Output of model

Output of this model is tensor of shape 1000, with confidence scores over Imagenet's 1000 classes.

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Dataset (Train and Validation)

Models are pretrained on ImageNet. For training we use train+valset in COCO except for 5000 images from minivalset, and use the minivalset to test. Details of performance on COCO object detection are provided in this paper

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References

Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun. ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design. 2018.

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Contributors

Ksenija Stanojevic

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License

BSD 3-Clause License

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