The googlenet-v2-tf
model is one of the Inception family, designed to perform image classification. Like the other Inception models, the googlenet-v2-tf
model has been pretrained on the ImageNet image database. Originally redistributed as a checkpoint file, was converted to frozen graph. For details about this family of models, check out the paper, repository.
freeze.py.patch
patch Metric | Value |
---|---|
Type | Classification |
GFLOPs | 4.058 |
MParams | 11.185 |
Source framework | TensorFlow* |
Metric | Original model | Converted model |
---|---|---|
Top 1 | 74.09% | 74.09% |
Top 5 | 91.80% | 91.80% |
Image, name - input
, shape - 1,224,224,3
, format is B,H,W,C
where:
B
- batch sizeH
- heightW
- widthC
- channelChannel order is RGB
. Mean values - [127.5, 127.5, 127.5], scale value - 127.5
Image, name - data
, shape - 1,3,224,224
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
Object classifier according to ImageNet classes, name - InceptionV2/Predictions/Softmax
, shape - 1,1001
, output data format is B,C
where:
B
- batch sizeC
- Predicted probabilities for each class in [0, 1] rangeObject classifier according to ImageNet classes, name - InceptionV2/Predictions/Softmax
, shape - 1,1001
, output data format is B,C
where:
B
- batch sizeC
- Predicted probabilities for each class in [0, 1] rangeThe original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TensorFlow.txt.