Skip to content

Latest commit

 

History

History
95 lines (65 loc) · 3.12 KB

README.md

File metadata and controls

95 lines (65 loc) · 3.12 KB

resnet-50-tf

Use Case and High-Level Description

resnet-50-tf is a TensorFlow* implementation of ResNet-50 - an image classification model pre-trained 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

  1. Install TensorFlow*, version 1.14.0.
  2. Download pre-trained weights
  3. Run example conversion code, available 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

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%

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

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-TF-Models.txt.