shufflenet_v2

ShuffleNet

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.

PyTorch ShuffleNet-v2 ==> ONNX ShuffleNet-v2

Model

Model	Download	ONNX version	Opset version	Top-1 error	Top-5 error
ShuffleNet-v2-fp32	8.79MB	1.9	12	33.65	13.43

Preprocessing

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]

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)

Dataset

ImageNet (ILSVRC2012).

References

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

• huffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices]

• Intel® Neural Compressor

Contributors

• Ksenija Stanojevic
• mengniwang95 (Intel)
• airMeng (Intel)
• ftian1 (Intel)
• hshen14 (Intel)

License

BSD 3-Clause License