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
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";
Going deeper with convolutions
Caffe BVLC GoogLeNet ==> Caffe2 GoogLeNet ==> ONNX GoogLeNet
data_0: float[1, 3, 224, 224]
prob_0: float[1, 1000]
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
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.)