The densenet-161
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-161
is much larger at 100MB 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-161
is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.
Metric | Value |
---|---|
Type | Classification |
GFLOPs | 15.561 |
MParams | 28.666 |
Source framework | Caffe* |
Metric | Value |
---|---|
Top 1 | 77.55% |
Top 5 | 93.92% |
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 - 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
The original model is distributed under the following license: