v0.2.1

Code license changed to MIT License.

New features

Classes and functions

• Rotate90: New transform to rotate image by 90 degrees.
• Trainer: Option for validating on loss value.

Tools

• infer.py: New optional cmd argument to rotate image before inference.

Minor Fixes

• Change UAV dataset hosting (now in ULiège Open Data Repository)
• It is now possible to resume a training session when a validation frequency > 1 is used.
• Solving the problem of continuously increasing training time over epochs.

Commits

Alexandre-Delplanque (19):

• 0fb5a68 - Merge branch 'feature' into main
• 506e1e1 - chore: update CHANGELOG.md
• c101dca - fix: update code version
• 031cc1a - chore: minor change
• 4a2c68a - fix: update code version
• b424c5b - Merge pull request #3 from Alexandre-Delplanque/new-license
• ce16958 - chore: update CHANGELOG.md
• d2b9078 - chore: final licence changes
• f063a82 - chore: switch to MIT License
• d6b3ffb - fix: change how images are rotated
• c67404b - feat: add Rotate90 transform
• 39e317a - fix: increasing training time over epochs
• 4baeab1 - fix: resume training when using valid_freq
• d86ec37 - fix: new link for UAV dataset #2
• 53dd60a - fix: weights to device when not None (_ssim_loss)
• b082fc4 - feat: rotation option for infer.py tool
• c0bfc31 - fix: only CSV's images when -all arg is False
• addbc9b - Merge branch 'feature' of https://github.com/Alexandre-Delplanque/Herd-Net into feature
• d480319 - feat: option for evaluation with loss in Trainer

v0.2.0

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) 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.

v0.1.0

Add LICENSE