The efficientnet-b0-pytorch model is one of the EfficientNet models designed to perform image classification. This model was pretrained in 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 [3x224x224] 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-b0-pytorch is the typical object classifier output for
1000 different classifications matching those in the ImageNet database.
| Metric | Value |
|---|---|
| Type | Classification |
| GFLOPs | 0.819 |
| MParams | 5.268 |
| Source framework | PyTorch* |
| Metric | Original model | Converted model |
|---|---|---|
| Top 1 | 76.91 | 76.91 |
| Top 5 | 93.21 | 93.21 |
Image, name - data, shape - 1,3,224,224, 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 - 1,3,224,224, 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, 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.