The alexnet model is designed to perform image classification. Just like other common classification models, the alexnet model has been pre-trained on the ImageNet image database. For details about this model, check out the paper.
The model input is a blob that consists of a single image of 1, 3, 227, 227 in BGR order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before passing the image blob into the network.
The model output for alexnet is the usual object classifier output for the 1000 different classifications matching those in the ImageNet database.
| Metric | Value |
|---|---|
| Type | Classification |
| GFLOPs | 1.5 |
| MParams | 60.965 |
| Source framework | Caffe* |
| Metric | Value |
|---|---|
| Top 1 | 56.598% |
| Top 5 | 79.812% |
See the original model's documentation.
Image, name - data, shape - 1, 3, 227, 227, format is B, C, H, W, where:
B- batch sizeC- channelH- heightW- width
Channel order is BGR.
Mean values - [104, 117, 123]
Image, name - data, shape - 1, 3, 227, 227, format is B, C, H, W, where:
B- batch sizeC- channelH- heightW- width
Channel order is BGR.
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
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
You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
The original model is distributed under the following license:
This model is released for unrestricted use.