# 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 pre-trained on the ImageNet image database. For details about this family of models, check out the repository.

Metric

Value

Type

Classification

GFLOPs

5.724

MParams

7.971

Source framework

Caffe*

Metric

Value

Top 1

74.42%

Top 5

92.136%

## Input¶

The model input is a blob that consists of a single image of 1, 3, 224, 224 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 - fc6, shape - 1, 1000, 1, 1, contains predicted probability for each class in logits format.

### Converted Model¶

Object classifier according to ImageNet classes, name - fc6, shape - 1, 1000, 1, 1, contains predicted probability for each class in logits format.

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>