Use Case and High-Level Description

This is a Caffe2* version of squeezenet1.1 model, designed to perform image classification. This model was converted from Caffe* to Caffe2* format. For details see repository https://github.com/caffe2/models/tree/master/squeezenet, paper https://arxiv.org/abs/1602.07360.

Example

Specification

Metric	Value
Type	Classification
GFLOPs	0.784
MParams	1.235
Source framework	Caffe2*

Accuracy

Metric	Value
Top 1	56.502%
Top 5	79.576%

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 - [103.96,116.78,123.68].

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 - softmaxout, 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 - softmaxout, 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 Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.