ResNet-50 1.2
ResNet-50 is a deep convolutional network for classification.
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