Use Case and High-Level Description

resnet-50-tf is a TensorFlow* implementation of ResNet-50 - an image classification model pretrained on the ImageNet dataset. Originally redistributed in Saved model format, converted to frozen graph using tf.graph_util module. For details see paper, repository.

Steps to Reproduce Conversion to Frozen Graph

Install TensorFlow*, version 1.14.0.
Download pretrained weights
Run example conversion code, avaliable at freeze_saved_model.py
python3 freeze_saved_model.py --saved_model_dir path/to/downloaded/saved_model --save_file path/to/resulting/frozen_graph.pb

Example

Specification

Metric	Value
Type	Classification
GFLOPs	8.2164
MParams	25.53
Source framework	TensorFlow*

Accuracy

Metric	Original model	Converted model
Top 1	76.45%	76.17%
Top 5	93.05%	92.98%

Performance

Input

Original Model

Image, name: map/TensorArrayStack/TensorArrayGatherV3, shape: 1,224,224,3, format is B,H,W,C where:

B - batch size
H - height
W - width
C - channel

Channel order is RGB. Mean values: [123.68,116.78,103.94].

Converted Model

Image, name: map/TensorArrayStack/TensorArrayGatherV3, shape: 1,224,224,3, format is B,H,W,C where:

B - batch size
H - height
W - width
C - channel

Channel order is BGR.

Output

Original Model

Object classifier according to ImageNet classes, name: softmax_tensor, shape: 1,1001, output data format is B,C where:

B - batch size
C - predicted probabilities for each class in [0, 1] range

Converted Model

Object classifier according to ImageNet classes, name: softmax_tensor, shape: 1,1001, output data format is B,C where:

B - batch size
C - predicted probabilities for each class in [0, 1] range

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TensorFlow.txt.