This is the Resnet-50 v1 model that is designed to perform image classification. The model has been pretrained on the ImageNet image database and then pruned to 60.4% of sparsity and quantized to INT8 fixed-point precision using so-called Quantization-aware training approach implemented in TensorFlow framework. The sparsity is represented by zeros inside the weights of Convolutional and Fully-conneted layers. For details about the original floating point model, check out the paper.
The model input is a blob that consists of a single image of "1x224x224x3" in BGR order.
The model output for resnet-50-int8-sparse-v2-tf-0001 is the usual object classifier output for the 1000 different classifications matching those in the ImageNet database.
| Metric | Value |
|---|---|
| Type | Classification |
| GFLOPs | 6.996 |
| MParams | 25.530 |
| Source framework | TensorFlow |
The quality metrics calculated on ImageNet validation dataset is 74.56% accuracy top-1.
| Metric | Value |
|---|---|
| Accuracy top-1 (ImageNet) | 74.56% |
Image, shape - 1,224,224,3, format is B,H,W,C where:
B - batch sizeH - heightW - widthC - channelChannel order is BGR
Object classifier according to ImageNet classes, shape -1,1000, output data format is B,C where:
B - batch sizeC - predicted probabilities for each class in [0, 1] range