The densenet-169
model is 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-169
is larger at just about 55MB 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-169
is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.
Metric | Value |
---|---|
Type | Classification |
GFLOPs | 6.788 |
MParams | 14.139 |
Source framework | Caffe* |
Metric | Value |
---|---|
Top 1 | 76.106% |
Top 5 | 93.106% |
Image, name - data
, shape - 1,3,224,224
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
. Mean values - [103.94,116.78,123.68], scale value - 58.8235294117647.
Image, name - data
, shape - 1,3,224,224
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
.
Object classifier according to ImageNet classes, name - fc6
, shape - 1,1000,1,1
, contains predicted probability for each class in logits format.
Object classifier according to ImageNet classes, name - fc6
, shape - 1,1000,1,1
, contains predicted probability for each class in logits format.
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
An example of using the Model Converter:
The original model is distributed under the following license: