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densenet-121

Use Case and High-Level Description

The densenet-121 model is one of the DenseNet* group of models designed to perform image classification. The authors originally trained the models on Torch*, but then converted them into Caffe* format. All DenseNet models have been pretrained on the ImageNet image database. For details about this family of models, check out the repository.

Example

Specification

Metric Value
Type Classification
GFLOPs 5.724
MParams 7.971
Source framework Caffe*

Accuracy

Metric Value
Top 1 74.42%
Top 5 92.136%

See the original repository.

Performance

Input

The model input is a blob that consists of a single image of 1x3x224x224 in BGR order. Before passing the image blob into the network, subtract BGR mean values as follows: [103.94, 116.78, 123.68]. In addition, values must be divided by 0.017.

Original Model

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

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

Channel order is BGR. Mean values - [103.94,116.78,123.68], scale value - 58.8235294117647

Converted Model

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

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

Channel order is BGR.

Output

The model output for densenet-121 is a typical object classifier output for 1000 different classifications matching those in the ImageNet database.

Original Model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000,1,1, contains predicted probability for each class in logits format.

Converted Model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000,1,1, contains predicted probability for each class in logits format.

Legal Information

The original model is distributed under the following license:

Copyright (c) 2016, Zhuang Liu.
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 DenseNet 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
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
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