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AlexNet

Download:

Model size: 244 MB

Description

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).

Paper

ImageNet Classification with Deep Convolutional Neural Networks

Dataset

ILSVRC2012

Source

Caffe BVLC AlexNet ==> Caffe2 AlexNet ==> ONNX AlexNet

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_0.npz
  • test_data_1.npz
  • test_data_2.npz
  • test_data_set_0
  • test_data_set_1
  • test_data_set_2

Results/accuracy on test set

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.)

License

BSD-3