Currently using:
- ISIC_MSK-2_1
identified as possibly usable:
- ISIC_MSK-1_1
- ISIC_MSK-1_2
- ISIC_MSK-2_1
- ISIC_MSK-4_1
- ISIC_UDA-1_1
- ISIC_UDA-2_1
metadata and information: view ISIC/csv_metadata/
To run the scripts and notebooks, follow these steps:
- read and execute ISIC/notebooks_metadata_and_images/get_3_images.ipynb
- sort files by e.g. using script ISIC/dataordering.py or split with ISIC/0_datasplitting.ipynb
- update ISIC/preparing.sh
- run notebooks in (numbered) order
After setting up a spot instance, copy the IP, paste it in a new tab in your browser, log in (deep_learning) and do the following:
- click New -> Terminal
- Copy, paste and execute the following lines:
wget https://raw.githubusercontent.com/linoba/melanoma-classification/master/ISIC/preparing.sh
chmod +x preparing.sh
./preparing.sh
pwd
Continue with:
- Copy the path that appeared after last line
- Open the notebook you want to run
- Make sure that the path to your datafolder is set to the path you copied above with the suffix "/data/"
- Run your tests
Our best result with file 4_110.h5 can be found here: https://files.fm/f/n69jjnpu
Code taken from tutorial.