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.

## Specification

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

Metric Value
Top 1 76.886%
Top 5 93.556%

## 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

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