cntk-hotel-pictures-classificator | using CNTK 2.1 to train model | Computer Vision library

Download content of this repo You can either clone this repo or just download it and unzip to some folder
Setup Python environment In order for scripts to work you should have a proper Python environment. If you don't already have it setup then you should follow one of the online tutorials. To setup Python environment and all the dependencies required by CNTK on my local Windows machine, I used scripted setup tutorial for Windows. If you're using Linux then you might want to look into one of these tutorials. Just bear in mind that this project was developed and tested with CNTK 2.1 and it wasn't tested for any other version. Even after setting up Python environment properly you might still witness some errors when running Python scripts. Most of those errors are related to missing modules or some 3rd party frameworks and tools (i.e. GraphViz). Missing modules can be easily pip installed and most of the required ones can be found in requirements.txt files for each folder with Python scripts. Please report if you'll find any errors or missing modules, thanks!
Download hotel pictures dataset (HotailorPOC2) and pretrained AlexNet model used for Transfer Learning Go to Detection/FasterRCNN folder in the location were you unzipped this repo and run install_data_and_model.py. It will automatically download the HotailorPOC2 dataset, pretrained AlexNet model and will generate mapping files required to train the model.
Follow steps number 1 and 2 from setup instructions.
In order to train the model we use transfer learning and we need to have a pretrained model for that. For this sample we use AlexNet model.
Make sure to change variables in your config.py file and make sure you set __C.CNTK.MAP_FILE_PATH variable to a proper directory: if __C.CNTK.DATASET == "HotailorPOC2": #name of your dataset. Must match the name set with property '__C.CNTK.DATASET' __C.CNTK.MAP_FILE_PATH = "../../DataSets/HotailorPOC2" # your dataset directory __C.CNTK.NUM_TRAIN_IMAGES = 82 # number of images in 'positive' folder __C.CNTK.NUM_TEST_IMAGES = 20 # number of images in 'testImages' folder __C.CNTK.PROPOSAL_LAYER_PARAMS = "'feat_stride': 16\n'scales':\n - 4 \n - 8 \n - 12"
Open install_data_and_model.py script and comment out those lines: #downloads hotel pictures classificator dataset (HotailorPOC2) #comment out lines bellow if you're using a custom dataset sys.path.append(os.path.join(base_folder, "..", "..", "DataSets", "HotailorPOC2")) from download_HotailorPOC2_dataset import download_dataset download_dataset()
Run install_data_and_model.py script. Bear in mind that downloading the pretrained model may take few minutes or even more depending on your internet connection.