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. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.
| Model | Download | ONNX version | Opset version | Top-1 accuracy (%) | Top-5 accuracy (%) |
|---|---|---|---|---|---|
| ResNet18-v2 | 44.7 MB | 1.2.1 | 7 | 69.93 | 89.29 |
- ResNetv2 Identity mappings in deep residual networks He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In European Conference on Computer Vision, pp. 630-645. Springer, Cham, 2016.
- MXNet, Gluon model zoo, GluonCV
- Intel® Neural Compressor
- onnx/models
- resnet18-v2-7.onnx
- ankkhedia (Amazon AI)
- abhinavs95 (Amazon AI)
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
Apache 2.0