Skip to content

Latest commit

 

History

History
85 lines (57 loc) · 2.56 KB

README.md

File metadata and controls

85 lines (57 loc) · 2.56 KB

efficientnet-b0

Use Case and High-Level Description

The efficientnet-b0 model is one of the EfficientNet models designed to perform image classification. This model was pre-trained in TensorFlow*. All the EfficientNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the TensorFlow Cloud TPU repository.

Specification

Metric Value
Type Classification
GFLOPs 0.819
MParams 5.268
Source framework TensorFlow*

Accuracy

Metric Original model Converted model
Top 1 75.70% 75.70%
Top 5 92.76% 92.76%

Input

Original Model

Image, name - image, 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.

Converted Model

Image, name - sub/placeholder_port_0, shape - 1, 3, 224, 224, format is B, C, H, W, where:

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

Channel order is BGR.

Output

Original Model

Object classifier according to ImageNet classes, name - logits, shape - 1, 1000, output data format is B, C, where:

  • B - batch size
  • C - predicted probabilities for each class in logits format

Converted Model

Object classifier according to ImageNet classes, name - efficientnet-b0/model/head/dense/MatMul, shape - 1, 1000, output data format is B, C, where:

  • B - batch size
  • C - predicted probabilities for each class in logits format

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-TF-TPU.txt.