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 <omz_dir>/models/public/resnet-50-tf/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 OpenVINO™ IR Format

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

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: