shufflenet

ShuffleNet

Use cases

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

Description

ShuffleNet is a deep convolutional network for image classification. ShuffleNetV2 is an improved architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification.

Caffe2 ShuffleNet-v1 ==> ONNX ShuffleNet-v1
PyTorch ShuffleNet-v2 ==> ONNX ShuffleNet-v2

Model

Model	Download	Download (with sample test data)	ONNX version	Opset version
ShuffleNet-v1	5.3 MB	7 MB	1.1	3
ShuffleNet-v1	5.3 MB	9 MB	1.1.2	6
ShuffleNet-v1	5.3 MB	9 MB	1.2	7
ShuffleNet-v1	5.3 MB	9 MB	1.3	8
ShuffleNet-v1	5.3 MB	9 MB	1.4	9

Model	Download	Download (with sample test data)	ONNX version	Opset version	Top-1 error	Top-5 error
ShuffleNet-v2	9.2MB	8.7MB	1.6	10	30.64	11.68

Inference

This script converts the ShuffleNetv2 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.

data_0: float[1, 3, 224, 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.

softmax_1: float[1, 1000]

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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.

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

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Contributors

Ksenija Stanojevic

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License

BSD 3-Clause License

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