| Model | Download | Download (with sample test data) | ONNX version | Opset version |
|---|---|---|---|---|
| AlexNet | 238 MB | 225 MB | 1.1 | 3 |
| AlexNet | 238 MB | 225 MB | 1.1.2 | 6 |
| AlexNet | 238 MB | 226 MB | 1.2 | 7 |
| AlexNet | 238 MB | 226 MB | 1.3 | 8 |
| AlexNet | 238 MB | 226 MB | 1.4 | 9 |
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]
Randomly generated sample 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.)