efficientnet-b5¶
Use Case and High-Level Description¶
The efficientnet-b5
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 |
21.252 |
MParams |
30.303 |
Source framework |
TensorFlow* |
Accuracy¶
Metric |
Original model |
Converted model |
---|---|---|
Top 1 |
83.33% |
83.33% |
Top 5 |
96.67% |
96.67% |
Input¶
Original Model¶
Image, name - image
, shape - 1, 456, 456, 3
, format is B, H, W, C
, where:
B
- batch sizeH
- heightW
- widthC
- channel
Channel order is RGB
.
Converted Model¶
Image, name - sub/placeholder_port_0
, shape - 1, 456, 456, 3
, format is B, H, W, C
, where:
B
- batch sizeH
- heightW
- widthC
- channel
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 sizeC
- predicted probabilities for each class in the logits format
Converted Model¶
Object classifier according to ImageNet classes, name - efficientnet-b5/model/head/dense/MatMul
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in the 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 <omz_dir>/models/public/licenses/APACHE-2.0-TF-TPU.txt
.