All you need are the pretrained weights, which you can find here or convert yourself from the caffe library using caffe-to-tensorflow.
If you convert them on your own, take a look on the structure of the .npy weights file (dict of dicts or dict of lists).
- Python 3
- TensorFlow >= 1.12rc0
- Numpy
alexnet.py: Class with the graph definition of the AlexNet.caffe_classes.py: List of the 1000 class names of ImageNet (copied from here).validate_alexnet_on_imagenet.ipynb: Notebook to test the correct implementation of AlexNet and the pretrained weights on some images from the ImageNet database.images/*: contains three example images, needed for the notebook.
If you do not want to touch the code any further than necessary you have to provide two .txt files to the script (train.txt and val.txt). Each of them list the complete path to your train/val images together with the class number in the following structure.
Example train.txt:
/path/to/train/image1.png 0
/path/to/train/image2.png 1
/path/to/train/image3.png 2
/path/to/train/image4.png 0
.
.
were the first column is the path and the second the class label.
The other option is that you bring your own method of loading images and providing batches of images and labels, but then you have to adapt the code on a few lines.