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densenet-201

Use Case and High-Level Description

The densenet-201 model is also one of the DenseNet group of models designed to perform image classification. The main difference with the densenet-121 model is the size and accuracy of the model. The densenet-201 is larger at over 77MB in size vs the densenet-121 model's roughly 31MB size. Originally trained on Torch, the authors converted them into Caffe* format. All the DenseNet models have been pretrained 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 1x3x224x224 in BGR order. The BGR mean values need to be subtracted as follows: [103.94, 116.78, 123.68] before passing the image blob into the network. In addition, values must be divided by 0.017.

The model output for densenet-201 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Example

Specification

Metric Value
Type Classification
GFLOPs 8.673
MParams 20.001
Source framework Caffe*

Accuracy

See https://github.com/shicai/DenseNet-Caffe.

Performance

Input

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

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.

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