Skip to content

Latest commit

 

History

History
55 lines (49 loc) · 2.33 KB

README.md

File metadata and controls

55 lines (49 loc) · 2.33 KB
Raw

How to train and evaluate models

  1. Prepare data (converting to grayscale, resizing and fusing the label files). This step assumes that your data is organized as follows:

    ├──data-root
    ├── DBdumps
    ├── ProlificExport
    ├── ReyFigures
    │ ├── data2018
    │ │ ├── newupload
    │ │ ├── newupload_15_11_2018
    │ │ ├── newupload_9_11_2018
    │ │ ├── uploadFinal
    │ │ └── uploadFinalREF
    │ └── data2021
    │     ├── KISPI
    │     ├── Tino_cropped
    │     ├── Typeform
    │     ├── USZ_fotos
    │     └── USZ_scans
    ├── UserRatingData
    └── simulated

    Run the following command

    python prepare_data.py --data-root /path/to/data/ --dataset data-2018-2021 --preprocessing 0 --image-size 116 150

    which will create a new directory serialized-data/data-2018-2021-116x150-pp0 in data-root, which contains the entire processed data and label files (train / test split).

  2. Train models, e.g. multilabel classifier, via the following command

    python train_multilabel.py --data-root /data-root/serialized-data/data-2018-2021-116x150-pp0 --results-dir /path/to/results --eval-test --id <id> --epochs 75 --batch-size 64 --lr 0.01 --gamma 0.95 --weighted-sampling 1 --image-size 116 150

    This will create a new dir in the specified results directory and save checkpoints + print outputs there. UPDATE: with hyperparameters.py and config.py one only has to run

    python train_multilabel.py

  3. To evaluate models, run the following command

    python eval_multilabel.py --data-root /data-root/serialized-data/data-2018-2021-116x150-pp0 --results-dir /path/to/results --image-size 116 150 --batch-size 100 --tta --validation --angles -2.5 -1 0 1 2.5

    UPDATE: with hyperparameters.py and config.py one only has to run

    python eval_multilabel.py

    This will compute the overall MSE (note: change this to MAE in future), and create csv files which contains the prediction for each figure in the test set.

Note: the same workflow applies to training the regressor and for using different input sizes.