squeezenet1.0

Use Case and High-Level Description

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.

Example

Specification

Metric Value
Type Classification
GFLOPs 1.737
MParams 1.248
Source framework Caffe*

Accuracy

Metric Value
Top 1 57.684%
Top 5 80.38%

Performance

Input

Original model

Image, name - data, shape - 1,3,227,227, format is B,C,H,W where:

Channel order is BGR. Mean values - [104, 117, 123]

Converted model

Image, name - data, shape - 1,3,227,227, format is B,C,H,W where:

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:

Converted model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000, output data format is B,C where:

Legal Information

The original model is distributed under the following license:

BSD LICENSE.
Redistribution and use in source and binary forms, with or without modification, are permitted
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