googlenet-v1-tf

Use Case and High-Level Description

The googlenet-v1-tf model is one of the Inception family, designed to perform image classification. Like the other Inception models, the googlenet-v1-tf model has been pretrained on the ImageNet image database. For details about this family of models, check out the paper, repository.

Specification

Metric Value
Type Classification
GFLOPs 3.016
MParams 6.619
Source framework TensorFlow*

Accuracy

Metric Original model Converted model
Top 1 69.81% 69.81%
Top 5 89.61% 89.61%

Input

Original model

Image, name - input, shape - 1,224,224,3, format is B,H,W,C where:

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

Channel order is RGB. Mean values - [127.5, 127.5, 127.5], scale value - 127.5

Converted model

Image, name - data, shape - 1,3,224,224, format is B,C,H,W where:

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

Channel order is BGR

Output

Original model

Object classifier according to ImageNet classes, name - InceptionV1/Logits/Predictions/Softmax, shape - 1,1001, output data format is B,C where:

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

Converted model

Object classifier according to ImageNet classes, name - InceptionV1/Logits/Predictions/Softmax, shape - 1,1001, output data format is B,C where:

  • B - batch size
  • C - Predicted probabilities for each class in [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-TensorFlow.txt.

The original model uses the TF-Slim library, which is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TFSlim.txt.