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.
| Model | Download | Top-1 error | Top-5 error |
|---|---|---|---|
| ShuffleNetv2 | model | 33.65 | 13.43 |
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)-
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun. ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design. 2018.
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huffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices]
- Ksenija Stanojevic
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
BSD 3-Clause License