ResNet-50 1.2

ResNet-50 is a deep convolutional network for classification. ResNet is a short form for Residual network and residual learning's aim was to solve image classifications. Residual Network learn from residuals instead of features. **Paper** [Deep Residual Learning for Image Recognition][1] **Dataset** [ILSVRC2015][2] **Source** Caffe2 ResNet-50 ==> ONNX ResNet-50 **Model input and output** **Input** * gpu_0/data_0: float[1, 3, 224, 224] **Output** * gpu_0/softmax_1: float[1, 1000] **Pre-processing steps** **Post-processing steps** **Sample test data** random generated sample test data: * test_data_0.npz * test_data_1.npz * test_data_2.npz * test_data_set_0 * test_data_set_1 * test_data_set_2 **Model size: 103 MB** **License** [MIT][3] [1]: https://arxiv.org/abs/1512.03385 [2]: http://www.image-net.org/challenges/LSVRC/2015/ [3]:https://github.com/onnx/models/blob/master/LICENSE