README.md

GoogleNet

Download:

• release 1.1: https://s3.amazonaws.com/download.onnx/models/opset_3/bvlc_googlenet.tar.gz
• release 1.1.2: https://s3.amazonaws.com/download.onnx/models/opset_6/bvlc_googlenet.tar.gz
• release 1.2: https://s3.amazonaws.com/download.onnx/models/opset_7/bvlc_googlenet.tar.gz
• release 1.3: https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_googlenet.tar.gz
• master: https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_googlenet.tar.gz

Model size: 28 MB

Description

GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014.

Differences:

• not training with the relighting data-augmentation;
• not training with the scale or aspect-ratio data-augmentation;
• uses "xavier" to initialize the weights instead of "gaussian";

Paper

Going deeper with convolutions

Dataset

ILSVRC2014

Source

Caffe BVLC GoogLeNet ==> Caffe2 GoogLeNet ==> ONNX GoogLeNet

Model input and output

Input

data_0: float[1, 3, 224, 224]

Output

softmaxout_1: float[1, 1000]

Pre-processing steps

Post-processing steps

Sample test data

random generated sampe test data:

• test_data_set_0
• test_data_set_1
• test_data_set_2
• test_data_set_3
• test_data_set_4
• test_data_set_5

Results/accuracy on test set

This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)

License

BSD-3