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 28.4% 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-v1-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 75.05% accuracy top-1.
Metric | Value |
---|---|
Accuracy top-1 (ImageNet) | 75.05% |
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