This repo has the code of the Tensotflow implementation of the AlexNet CNN (Tensorflow > 1.2) and scripts to extract the features from C1, C5 and FC2 layers. The code is ready-to-go, including the trained weights of AlexNet and some images to test.

• The AlexNet weights were downloaded from: http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
• The TensorFlow implementation of Alexnet was firstly developed by: https://github.com/kratzert/finetune_alexnet_with_tensorflow

• Install pip dependences: $ pip install tf tqdm
• Install opencv2. Follow tutorial from http://milq.github.io/install-opencv-ubuntu-debian/.
• Download the Alexnet weights and locate them on the root of this project. Weights can be downloaded from http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy. $ cd cnn_feature_extractor_rpv_2017/ && wget http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy

• Put the images on a folder, every class separated by folder name: 000, 001, 002, 003, etc.
• Create a new config file in JSON format (examples in config/ folder).
• Run by executing python main.py -c config/*a_config_file.json*

• python main.py -c config/alexnet_iris.json

All the results and plots are defined on the config file, but generally they are located in the plots/ and results/ folders.

• plots: are PDF generated files with a normalized confusion matrix using a heatmap color map.
• results: are JSON files generated with all the relevant information about the experiment: confusion matrix, overall precision, average precision, etc.

• Héctor Azpúrua
• Patricia Almeida
• Willian Hofner