Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required.
Resnet34-v1-7
| Model | ONNX version | Opset version | Top-1 accuracy (%) | Top-5 accuracy (%) |
|---|---|---|---|---|
| Resnet34 | 1.2.1 | 7 | 73.73 | 91.40 |
- Dataset used for train and validation: ImageNet (ILSVRC2012).
Apache 2.0