What is the example about?
Would you be open to a self-contained example showing how to apply an AlbumentationsX pipeline to an image, bounding boxes, and class labels together, then inspect the original and augmented annotations in a W&B Table?
A common object-detection data-pipeline failure is that a geometric augmentation changes the image while bounding boxes or their class labels are transformed incorrectly. W&B's interactive bounding-box overlays make this easy to inspect before training.
Proposed scope
The example would:
- generate a small deterministic detection scene in the notebook, avoiding an external dataset download and data-license dependency;
- configure AlbumentationsX with
bbox_params=A.BboxParams(coord_format="pascal_voc", label_fields=["class_labels"]);
- apply a small augmentation pipeline to
image, bboxes, and class_labels in one call;
- convert the original and transformed Pascal VOC boxes to W&B's pixel-domain
box_data format;
- log both variants as
wandb.Image objects in a wandb.Table;
- store the AlbumentationsX pipeline configuration in the same W&B run; and
- omit model training so the example stays focused and quick to execute.
The resulting table would let readers inspect the box overlays and confirm that every transformed box retains the correct class label.
This could be a Colab notebook or a marimo example, whichever format the maintainers prefer.
Dependency details
The example would state that the current public AlbumentationsX package is AGPL-3.0-only. Importing the albumentations module provided by AlbumentationsX also requires an installed PyTorch runtime. PyTorch is intentionally not selected by the AlbumentationsX package metadata because users need the CPU, CUDA, or MPS build appropriate for their environment.
Contribution
I can prepare and run the example end to end, follow the repository's notebook formatting and dependency rules, and add it to the appropriate example index.
Would this scope be welcome? If so, would you prefer Colab or marimo?
What is the example about?
Would you be open to a self-contained example showing how to apply an AlbumentationsX pipeline to an image, bounding boxes, and class labels together, then inspect the original and augmented annotations in a W&B Table?
A common object-detection data-pipeline failure is that a geometric augmentation changes the image while bounding boxes or their class labels are transformed incorrectly. W&B's interactive bounding-box overlays make this easy to inspect before training.
Proposed scope
The example would:
bbox_params=A.BboxParams(coord_format="pascal_voc", label_fields=["class_labels"]);image,bboxes, andclass_labelsin one call;box_dataformat;wandb.Imageobjects in awandb.Table;The resulting table would let readers inspect the box overlays and confirm that every transformed box retains the correct class label.
This could be a Colab notebook or a marimo example, whichever format the maintainers prefer.
Dependency details
The example would state that the current public AlbumentationsX package is AGPL-3.0-only. Importing the
albumentationsmodule provided by AlbumentationsX also requires an installed PyTorch runtime. PyTorch is intentionally not selected by the AlbumentationsX package metadata because users need the CPU, CUDA, or MPS build appropriate for their environment.Contribution
I can prepare and run the example end to end, follow the repository's notebook formatting and dependency rules, and add it to the appropriate example index.
Would this scope be welcome? If so, would you prefer Colab or marimo?