googlenet-v1¶
Use Case and High-Level Description¶
The googlenet-v1
model is the first of the Inception family of models designed to perform image classification. Like the other Inception models, the googlenet-v1
model has been pre-trained on the ImageNet image database. For details about this family of models, check out the paper.
The model input is a blob that consists of a single image of 1, 3, 224, 224
in BGR
order. The BGR mean values need to be subtracted as follows: [104.0, 117.0, 123.0] before passing the image blob into the network.
The model output for googlenet-v1
is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.
Specification¶
Metric |
Value |
---|---|
Type |
Classification |
GFLOPs |
3.266 |
MParams |
6.999 |
Source framework |
Caffe* |
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>