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* |
See https://github.com/shicai/DenseNet-Caffe.
Image, name - data, shape - 1,3,224,224, format is B,C,H,W where:
B- batch sizeC- channelH- heightW- width
Channel 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- width
Channel order is BGR
Object classifier according to ImageNet classes, name - prob, shape - 1,1000,1,1, contains predicted
probability for each class in logits format
Object classifier according to ImageNet classes, name - prob, shape - 1,1000,1,1, contains predicted
probability for each class in logits format
https://raw.githubusercontent.com/liuzhuang13/DenseNet/master/LICENSE