Use Case and High-Level Description

This is a TensorFlow* version of densenet-169 model, one of the DenseNet* group of models designed to perform image classification. For details, see TensorFlow* API docs, repository and paper.

Specification

Metric	Value
Type	Classification
GFlops	6.7932
MParams	14.1389
Source framework	TensorFlow*

Accuracy

Metric	Value
Top 1	76.14%
Top 5	93.12%

Input

Original Model

Image, name: input_1 , shape: [1x224x224x3], format: [BxHxWxC], where:

B - batch size
H - image height
W - image width
C - number of channels

Expected color order: RGB. Mean values - [123.68, 116.78, 103.94], scale values - [58.395,57.12,57.375].

Converted Model

Image, name: input_1, shape: [1x3x224x224], [BxCxHxW], where:

B - batch size
C - number of channels
H - image height
W - image width

Expected color order: BGR.

Output

Original Model

Object classifier according to ImageNet classes, name - StatefulPartitionedCall/densenet169/predictions/Softmax, shape - 1,1000, output data format is B,C where:

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

Converted Model

The converted model has the same parameters as the original model.

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 APACHE-2.0-TensorFlow.txt.