se-resnet-101¶
Use Case and High-Level Description¶
Specification¶
Metric |
Value |
---|---|
Type |
Classification |
GFLOPs |
15.239 |
MParams |
49.274 |
Source framework |
Caffe* |
Accuracy¶
Metric |
Value |
---|---|
Top 1 |
78.252% |
Top 5 |
94.206% |
Input¶
Original model¶
Image, name - data
, shape - 1, 3, 224, 224
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
. Mean values - [104.0, 117.0, 123.0].
Converted model¶
Image, name - data
, shape - 1, 3, 224, 224
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
Output¶
Original model¶
Object classifier according to ImageNet classes, name - prob
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in [0, 1] range
Converted model¶
Object classifier according to ImageNet classes, name - prob
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in [0, 1] range
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-SENet.txt
.