The efficientnet-b5-pytorch
model is one of the EfficientNet
models designed to perform image classification. This model was pretrained in TensorFlow*, then weights were converted to PyTorch*. All the EfficientNet models have been pretrained on the ImageNet* image database. For details about this family of models, check out the EfficientNets for PyTorch repository.
The model input is a blob that consists of a single image with the [3x456x456] shape in the RGB order. Before passing the image blob to the network, do the following:
- Subtract the RGB mean values as follows: [123.675,116.28,103.53]
- Divide the RGB mean values by [58.395,57.12,57.375]
The model output for efficientnet-b5-pytorch
is the typical object classifier output for
the 1000 different classifications matching those in the ImageNet database.
Metric | Value |
---|---|
Type | Classification |
GFLOPs | 21.252 |
MParams | 30.303 |
Source framework | PyTorch* |
Metric | Original model | Converted model |
---|---|---|
Top 1 | 83.69% | 83.69% |
Top 5 | 96.71% | 96.71% |
Image, name - data
, shape - 1,3,456,456
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is RGB
.
Mean values - [123.675,116.28,103.53], scale values - [58.395,57.12,57.375].
Image, name - data
, shape - 1,3,456,456
, 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
, output data format is B,C
where:
B
- batch sizeC
- predicted probabilities for each class in the [0, 1] range
Object classifier according to ImageNet classes, name - prob
, shape - 1,1000
, output data format is B,C
where:
B
- batch sizeC
- predicted probabilities for each class in the [0, 1] range
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-PyTorch-EfficientNet.txt.