The alexnet model is designed to perform image classification. Just like other common classification models, the alexnet model has been pretrained 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 1x3x227x227 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
The original model is distributed under the following license:
This model is released for unrestricted use.