This folder contains the script to train and test Alexnet on ImageNet. This code was developed using the classic TensorFlow framework, so defining a computational graph and then executing it in a tf.Session.

To train AlexNet just run the command:

python train.py option

with options -scratch to train the model from scratch or -resume to resume the training from a checkpoint.

I trained AlexNet with the hyperparameters set in the script for ~46000 steps (roughly 46 epochs), decreasing the learning rate two times (by a factor of 10) when the loss became stagnant. The training image were preprocessed subtracting the training-set mean for each channel. No data-augmentation was performed (future improvement). The training was carried on a NVIDIA Tesla K40c (thanks to Avires Lab) and took a few days.

To evaluate the accuracy of the trained model I used the ILSVRC validation set (no test set is available). Run simply:

python test.py

This evaluates Top-1 and Top-k (you can set k inside the script) accuracy and error-rate. Inside the script you can also play with the K_CROPS parameter to see how the accuracy change when the predictions are averaged through different random crops of the images.

I tested the trained model on the ILSVRC validation set consisting of 50000 images. I obtained a Top-1 accuracy of 57.31% and a Top-5 accuracy of 80.31%, averaging the predictions on 5 random crops. With more epochs and some tweaks they can be improved of a few more points.

To predict the classes of an input image run:

python classify.py image

where image is the path of the image you want to classify.

e. g. that command on the lussari.jpg image gives the output:

AlexNet saw:
alp - score: 0.575796604156
church, church building - score: 0.0516746938229
valley, vale - score: 0.0432425364852
castle - score: 0.0284509658813
monastery - score: 0.0265731271356

Again, you can change the number of random crops produced and the Top-k prediction retrieved (here are both 5).

train.py and test.py scripts assume that ImageNet dataset folder is structured in this way:

ILSVRC2012
    ILSVRC2012_img_train
        n01440764
        n01443537
        n01484850
        ...
    ILSVRC2012_img_val
        ILSVRC2012_val_00000001.JPEG
        ILSVRC2012_val_00000002.JPEG
        ...
    data
        meta.mat
        ILSVRC2012_validation_ground_truth.txt