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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

Performance

Input

Original model

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

  • B - batch size
  • C - channel
  • H - height
  • W - width

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:

  • B - batch size
  • C - channel
  • H - height
  • W - 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 size
  • C - 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 size
  • C - Predicted probabilities for each class in [0, 1] range

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
provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions
and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
and the following disclaimer in the documentation and/or other materials provided with the
distribution.

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IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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