config.ini

[DATA]
# YOU ONLY NEED TO CHANGE THE FOLLOWING PATH FOR THE TRAINING
train_ann = '/content/TACO-Train-coco-filtered-2023_02_10 12_17_12.json'

# THE REST SHOULD REMAIN FIXED
train_data_folder = '/content/TACO-Train'
validation_ann = '/content/TACO-Validation/TACO-Validation-coco.json'
validation_data_folder = '/content/TACO-Validation'


[LOGGING]
# calculating mAP takes too much time, so we calculate it in every nth step
# calculating train set mAP is optional, disabling it will significantly increase training time

wandb_project = Encord-Active-Challenge
log_train_map = False
performance_tracking_interval = 2


[TRAIN]
# early_stopping_thresh are based on the performance_tracking_interval.
# Total epoch to terminate will be performance_tracking_interval*early_stopping_thresh

max_epoch = 30
early_stopping_thresh = 8


[INFERENCE]
# ===== Fill this part after the training, for the inference =====
# ================================================================
#
# target_data_folder and target_ann: Path to root image folder and COCO annotations for the dataset
# wandb_id: The unique wandb id that can be found in wandb platform, we append this to pickle file to not to confuse prediction files
# model_checkpoint_path: Path to model checkpoint file with .ckpt extension
# ontology_filepath: Encord ontology file, can be found in the project folder
# confidence_threshold: Score threshold for the each instance (Mask-RCNN outputs score for each prediction)

target_data_folder = /content/TACO-Validation
target_ann = '/content/TACO-Validation/TACO-Validation-coco.json'
model_checkpoint_path = /content/best_maskrcnn.ckpt
ontology_filepath = /content/TACO-Validation/ontology.json
confidence_threshold = 0.5