Download:
- release 1.1: https://s3.amazonaws.com/download.onnx/models/opset_3/bvlc_alexnet.tar.gz
- release 1.1.2: https://s3.amazonaws.com/download.onnx/models/opset_6/bvlc_alexnet.tar.gz
- release 1.2: https://s3.amazonaws.com/download.onnx/models/opset_7/bvlc_alexnet.tar.gz
- release 1.3: https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_alexnet.tar.gz
- master: https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_alexnet.tar.gz
Model size: 244 MB
AlexNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012.
Differences:
- not training with the relighting data-augmentation;
- initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).
ImageNet Classification with Deep Convolutional Neural Networks
Caffe BVLC AlexNet ==> Caffe2 AlexNet ==> ONNX AlexNet
data_0: float[1, 3, 224, 224]
softmaxout_1: float[1, 1000]
random generated sampe test data:
- test_data_0.npz
- test_data_1.npz
- test_data_2.npz
- test_data_set_0
- test_data_set_1
- test_data_set_2
The bundled model is the iteration 360,000 snapshot. The best validation performance during training was iteration 358,000 with validation accuracy 57.258% and loss 1.83948. This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% 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.)