CustomLogger : Argument to disable logging to CSV files (use to much memory).
Trainer: Arguments to set the validation frequency during training (valid_freq) and to choose whether to save logs to CSV files (csv_logger).
HerdNet: New method for reshaping classes (reshape_classes()), useful for loading pre-trained parameters.
FolderDataset: New flag (from_folder) in self.data attribute.
Python modules
sampler.py: New python module for hosting samplers for data loading.
Tools
train.py: New keys: wandb_run, model.freeze (HerdNet only), datasets.class_def, datasets.sampler and training_settings.valid_freq. Now use the class definition (i.e., datasets.class_def) to make sure the labels match the species names.
test.py: New keys: wandb_run and dataset.class_def. Now use the class definition (i.e., dataset.class_def) 1) to make sure the labels match the species names, and 2) for plotting precision-recall curves, saving the detections, the metrics and the confusion matrix.
New features
Classes and functions
CustomLogger: Argument to disable logging to CSV files (use to much memory).Trainer: Arguments to set the validation frequency during training (valid_freq) and to choose whether to save logs to CSV files (csv_logger).HerdNet: New method for reshaping classes (reshape_classes()), useful for loading pre-trained parameters.FolderDataset: New flag (from_folder) inself.dataattribute.Python modules
sampler.py: New python module for hosting samplers for data loading.Tools
train.py: New keys:wandb_run,model.freeze(HerdNet only),datasets.class_def,datasets.samplerandtraining_settings.valid_freq. Now use the class definition (i.e.,datasets.class_def) to make sure the labels match the species names.test.py: New keys:wandb_runanddataset.class_def. Now use the class definition (i.e.,dataset.class_def) 1) to make sure the labels match the species names, and 2) for plotting precision-recall curves, saving the detections, the metrics and the confusion matrix.