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.gitattributes

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 # Video files - compressed
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+annotations.coco.json filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
 license: mit
+pretty_name: BeeMachine Partwhole Dataset
+task_categories:
+- image-segmentation
+- image-classification
+tags:
+- biology
+- entomology
+- bumble-bee
+- fine-grained
+- part-segmentation
+- citizen-science
+size_categories:
+- 1K<n<10K
 ---
+
+# BeeMachine Partwhole Dataset
+
+Pixel-level anatomical part annotations for bumble bee photographs used by the BeeMachine identification service and by the IAAI-27 evaluation of anatomy-guided learning strategies.
+
+## Summary
+
+| Property | Value |
+|---|---|
+| Images | 7,716 |
+| Species | 160 |
+| Part classes | background, abdomen, head, thorax |
+| Mask format | integer PNG (`*_m.png`), same stem as the RGB image |
+| Labels | `species_labels.csv` (`images`, `species`) |
+
+## Directory layout
+
+Files in this repository:
+
+```
+README.md
+species_labels.csv      # image filename → species
+annotations.coco.json   # COCO-format segmentation export
+images.tar.gz           # RGB photographs (flat directory `images/`)
+masks.tar.gz            # integer part masks (flat directory `masks/`)
+```
+
+After download, unpack next to the CSV:
+
+```bash
+tar -xzf images.tar.gz
+tar -xzf masks.tar.gz
+```
+
+This yields:
+
+```
+images/                 # RGB photographs (flat)
+masks/                  # integer part masks, one {stem}_m.png per image
+species_labels.csv
+annotations.coco.json
+```
+
+
+Mask class IDs:
+
+| ID | Label |
+|---:|---|
+| 0 | background |
+| 1 | abdomen |
+| 2 | head |
+| 3 | thorax |
+
+## Intended use
+
+Train and evaluate part segmenters that supply anatomical masks to species classifiers. In the associated paper, this corpus is the Stage A segmentation set only; species classification is trained on a separate, larger originals-only BeeMachine classification corpus.
+
+**Note:** The full BeeMachine classification dataset is very large and is not hosted here; it will be available upon request.
+
+## Splits
+
+The paper uses a frozen species-stratified 75% / 15% / 10% train / validation / test split (seed 42). Those fold filenames are distributed with the supplementary code under `outputs/beemachine/frozen_splits/`. Train-only six-fold geometric augmentation (original, flips, 90/180/270° rotations) is generated from the frozen training fold and is not shipped here.
+
+## Source photographs
+
+Images were collected from citizen-science and museum archives used by BeeMachine (including Bumble Bee Watch, iNaturalist, and BugGuide). Part masks were annotated for head, thorax, and abdomen. Copyright in the underlying photographs remains with the original rights holders; redistribution here is for non-commercial research.
+
+## Citation
+
+If you use this dataset, please cite the BeeMachine paper and the IAAI-27 evaluation that releases these part annotations:
+
+```bibtex
+@article{spiesman2021beemachine,
+  author  = {Spiesman, Brian J. and Gratton, Claudio and Hatfield, Richard G. and Hsu, William H. and Jepsen, Sarina and McCornack, Brian and Patel, Krishna and Wang, Guanjie},
+  title   = {Assessing the Potential for Deep Learning and Computer Vision to Identify Bumble Bee Species from Images},
+  journal = {Scientific Reports},
+  volume  = {11},
+  pages   = {7580},
+  year    = {2021},
+  doi     = {10.1038/s41598-021-87210-1}
+}
+```
+
+## License
+
+MIT for the annotation files and packaging in this repository. Photograph copyrights remain with their original owners.

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