caffenet¶
Use Case and High-Level Description¶
CaffeNet* model is used for classification. For details see paper.
Specification¶
Metric |
Value |
---|---|
Type |
Classification |
GFlops |
1.463 |
MParams |
60.965 |
Source framework |
Caffe* |
Accuracy¶
Metric |
Value |
---|---|
Top 1 |
56.714% |
Top 5 |
79.916% |
Input¶
Original Model¶
Image, name: data
, shape: 1, 3, 227, 227
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
. Mean values: [104.0, 117.0, 123.0].
Converted Model¶
Image, name: data
, shape: 1, 3, 227, 227
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Output¶
Original Model¶
Object classifier according to ImageNet classes, name: prob
, shape: 1, 1000
. Contains predicted probability for each class.
Converted model¶
Object classifier according to ImageNet classes, name: prob
, shape: 1, 1000
. Contains predicted probability for each class.
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>