Metrics (accuracy, IoU, dice_coef) are currently computed for the whole images, without any distinction between classes. To gain on description quality, and be able to compare with state-of-the-art results and contest leaderboards (see Mapillary, CityScapes, or AerialImage examples), we should add class-specific metric results in training_metrics.csv.
It should be interesting to add the best instance metrics in the best-instance-<img_size>-<aggregation>.json file (produced by paramoptim.py when exploring hyperparameters) as well.
Metrics (accuracy, IoU, dice_coef) are currently computed for the whole images, without any distinction between classes. To gain on description quality, and be able to compare with state-of-the-art results and contest leaderboards (see Mapillary, CityScapes, or AerialImage examples), we should add class-specific metric results in
training_metrics.csv.It should be interesting to add the best instance metrics in the
best-instance-<img_size>-<aggregation>.jsonfile (produced byparamoptim.pywhen exploring hyperparameters) as well.