Skip to content

Latest commit

 

History

History
86 lines (64 loc) · 3.11 KB

README.md

File metadata and controls

86 lines (64 loc) · 3.11 KB
Raw

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection: Task 3

This project contains the source code used for the RECOD Titan's submission to ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection (Task 3). This project was forked from the source of the paper 'Data Augmentation for Skin Lesion Analysis'.

Project setup

  1. Install OpenCV with pip3 install opencv-python.
  2. Run pip3 install -r requirements.txt.
  3. Download data from ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection.

Train

The project uses Sacred to organize the experiments. The main script for training is in the train.py file. Check the available settings by running python3 train.py print_config.

Possible values for model_names: resnet152, inceptionv4, densenet161.

Example: training ResNet-152 with split 1

TRAIN_ROOT=/path/to/dataset/images
TRAIN_CSV=splits/split_task3_train_full_1.txt
VAL_ROOT=/path/to/dataset/images
VAL_CSV=splits/split_task3_validation.txt

python3 train.py with \
    train_root=$TRAIN_ROOT train_csv=$TRAIN_CSV \
    val_root=$VAL_ROOT val_csv=$VAL_CSV \
    model_name='resnet152' \
    'aug={"color_contrast": 0.3, "color_saturation": 0.3, "color_brightness": 0.3, "color_hue": 0.1, "rotation": 90, "scale": (0.8, 1.2), "shear": 20, "vflip": True, "hflip": True, "random_crop": True}' \
    weighted_loss=True \
    --name resnet152-split-1

If everything goes well, Sacred will create a directory with a unique ID inside results (e.g. results/1 for the first run). Inside this directory, you will find:

  • config.json: Sacred configuration used in training.
  • cout.txt: Entire stdout produced during the training.
  • run.json: General metadata of the training.
  • train.csv: CSV with metrics on train set.
  • val.csv: CSV with metrics on validation set.
  • checkpoints/model_best.pth: model with the best validation AUC.
  • checkpoints/model_last.pth: model as in the last epoch.

Telegram API

If you want to monitor the experiments with Telegram (receive a message when the experiments start, finish, or fail), create a file telegram.json at the root of the project:

$ cat telegram.json
{
    "token": "00000000:XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
    "chat_id": "00000000"
}

To configure the Telegram API, check this.

Test

Each model file (i.e, model_best.pth or model_last.pth) contains the PyTorch model, weights, and augmentation configuration (accessed through model.aug_params). To load the model, use torch.load.

The test.py file will automatically infer the augmentation settings from the model. Run python3 test.py --help to check all available options.

Example: get predictions for test set

TEST_ROOT=/path/to/dataset/images
TEST_CSV=splits/split_task3_testsubmission_challenge.txt
python3 test.py results/<SACRED_ID>/checkpoints/model_best.pth $TEST_ROOT $TEST_CSV -n 128 --output results_test.csv