Computationally efficient CNN architecture designed specifically for mobile devices with very limited computing power.
ShuffleNetV2 is an architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification.
| Model | Download | Download (with sample test data) | ONNX version | Opset version | Top-1 error | Top-5 error |
|---|---|---|---|---|---|---|
| ShuffleNetV2 | 9.2MB | 8.7MB | 1.6 | 10 | 30.64 | 11.68 |
This script converts the model from PyTorch to ONNX and uses ONNX Runtime for inference.
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.
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 this model is tensor of shape 1000, with confidence scores over Imagenet's 1000 classes.
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
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun. ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design. 2018.
Ksenija Stanojevic
BSD 3-Clause License