The squeezenet1.0
model is one of the SqueezeNet topology models, is designed to perform image classification. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the repository.
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 squeezenet1.0
is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.
Metric | Value |
---|---|
Type | Classification |
GFLOPs | 1.737 |
MParams | 1.248 |
Source framework | Caffe* |
Metric | Value |
---|---|
Top 1 | 57.684% |
Top 5 | 80.38% |
Image, name - data
, shape - 1,3,227,227
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel 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
- widthChannel 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] rangeObject 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] rangeThe original model is distributed under the following license: