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googlenet-v1

Use Case and High-Level Description

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

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: [104.0,117.0,123.0] before passing the image blob into the network.

The model output for googlenet-v1 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Example

Specification

Metric Value
Type Classification
GFLOPs 3.266
MParams 6.999
Source framework Caffe*

Accuracy

See https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet.

Performance

Input

Original 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. Mean values - [104.0,117.0,123.0]

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 - prob, shape - 1,1000, 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 - prob, shape - 1,1000, 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 following license:

This model is released for unrestricted use.