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mobilenet-v1-1.0-224

Use Case and High-Level Description

mobilenet-v1-1.0-224 is one of MobileNet V1 architecture with the width multiplier 1.0 and resolution 224. It is small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used.

Example

Specification

Metric Value
Type Classification
GFlops 1.148
MParams 4.221
Source framework Caffe*

Performance

Input

Original model

Image, name - input , shape - 1,3,224,224, format B,C,H,W, where:

- B - batch size
- C - number of channels
- H - image height
- W - image width

Expected color order - BGR. Mean values - [103.94,116.78,123.68], scale factor for each channel - 58.8235294117647

Converted model

Image, name - input , shape - 1,3,224,224, format B,C,H,W, where:

- B - batch size
- C - number of channels
- H - image height
- W - image width

Expected color order - 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 3-Clause License

Copyright (c) 2017-, Shicai Yang
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

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

* 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.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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