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densenet-161-tf

Use Case and High-Level Description

This is a TensorFlow* version of densenet-161 model, one of the DenseNet group of models designed to perform image classification. The weights were converted from DenseNet-Keras Models. For details see repository, paper.

Specification

Metric

Value

Type

Classification

GFlops

14.128

MParams

28.666

Source framework

TensorFlow*

Accuracy

Metric

Value

Top 1

76.446%

Top 5

93.228%

Input

Original Model

Image, name: Placeholder, shape: 1, 224, 224, 3, format: B, H, W, C, 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 factor for each channel: 58.8235294

Converted Model

Image, name: Placeholder, shape: 1, 3, 224, 224, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output

Original Model

Floating point values in range [0, 1], which represent probabilities for classes in a dataset. Name: densenet161/predictions/Reshape_1.

Converted Model

Floating point values in a range [0, 1], which represent probabilities for classes in a dataset. Name: densenet161/predictions/Reshape_1/Transpose, shape: 1, 1, 1, 1000.

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>
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