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efficientnet-b7_auto_aug

Use Case and High-Level Description

The efficientnet-b7_auto_aug model is one of the EfficientNet models designed to perform image classification, trained with the AutoAugmentation preprocessing. This model was pretrained in TensorFlow*. All the EfficientNet models have been pretrained on the ImageNet* image database. For details about this family of models, check out the TensorFlow Cloud TPU repository.

Example

Specification

Metric Value
Type Classification
GFLOPs 77.618
MParams 66.193
Source framework TensorFlow*

Accuracy

Metric Original model Converted model
Top 1 84.68 84.68
Top 5 97.09 97.09

Performance

Input

Original Model

Image, name - image, shape - [1x600x600x3], format is [BxHxWxC] where:

  • B - batch size
  • H - height
  • W - width
  • C - channel

Channel order is RGB.

Converted Model

Image, name - sub/placeholder_port_0, shape - [1x600x600x3], format is [BxHxWxC] where:

  • B - batch size
  • H - height
  • W - width
  • C - channel

Channel order is BGR.

Output

Original Model

Object classifier according to ImageNet classes, name - logits, shape - 1,1000, output data format is B,C where:

  • B - batch size
  • C - predicted probabilities for each class in the [0, 1] range

Converted Model

Object classifier according to ImageNet classes, name - efficientnet-b7/model/head/dense/MatMul, shape - 1,1000, output data format is B,C where:

  • B - batch size
  • C - predicted probabilities for each class in the [0, 1] range

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TF-TPU.txt.