| --- |
| task_categories: |
| - image-segmentation |
| tags: |
| - semantic-segmentation |
| - capybara |
| - computer-vision |
| - synthetic-data |
| pretty_name: OpenCAPY |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # OpenCAPY |
|
|
| OpenCAPY is a capybara semantic segmentation dataset. |
|
|
| ## Dataset Summary |
|
|
| OpenCAPY provides RGB images and single-channel PNG segmentation masks for capybara segmentation. Mask pixel values are class indices, not visualization values. |
|
|
| - Task: semantic segmentation |
| - Images: RGB image files under `images/` |
| - Masks: single-channel PNG files under `labels/` |
| - Label value `0`: background |
| - Label value `1`: capybara |
| - Training data includes real and synthetic data |
| - Validation and test data are real data |
|
|
| ## Dataset Structure |
|
|
| ```text |
| OpenCAPY/ |
| ├── README.md |
| ├── RELEASE_MANIFEST.txt |
| ├── RELEASE_REPORT.md |
| ├── dataset.json |
| ├── images/ |
| ├── labels/ |
| ├── metadata/ |
| └── splits/ |
| ``` |
|
|
| Each image, label, and metadata file uses the same relative stem. For example: |
|
|
| ```text |
| images/train_real/night/img_xxx.jpg |
| labels/train_real/night/img_xxx.png |
| metadata/train_real/night/img_xxx.json |
| ``` |
|
|
| Split files contain extensionless image ids, for example: |
|
|
| ```text |
| train_real/night/img_xxx |
| ``` |
|
|
| The `dataset.json` file lists samples with relative paths for images, labels, and metadata. |
|
|
| ## Dataset Splits |
|
|
| ```text |
| train_synthetic_qc_cleaned 23626 |
| train_real 6494 |
| val_real 390 |
| test_real 382 |
| ---------------------------------- |
| total 30892 |
| ``` |
|
|
| ## Labels |
|
|
| Masks are single-channel PNG files with class-index values: |
|
|
| ```text |
| 0 background |
| 1 capybara |
| ``` |
|
|
| Do not scale training labels to `0/255`. Visualization masks and preview overlays should be generated outside the canonical dataset files. |
|
|
| ## Data Sources |
|
|
| The release contains both real and synthetic capybara image subsets. The dataset metadata and split names distinguish real subsets from the `train_synthetic_qc_cleaned` synthetic training subset. |
|
|
| Confirmed subset names in this release: |
|
|
| - `train_real` |
| - `train_synthetic_qc_cleaned` |
| - `val_real` |
| - `test_real` |
|
|
| ## Annotation / Processing |
|
|
| The dataset contains segmentation masks stored as canonical `0/1` class-index PNG files. Repository metadata indicates that automated processing used Qwen grounding and SAM3 segmentation for real subsets, and that synthetic training data passed a QC-cleaned pipeline before inclusion. |
|
|
| This release packaging did not rerun annotation, regenerate masks, change split membership, or perform a new data-quality audit. Sample identity and split identity were preserved from the frozen source dataset. |
|
|
| ## Intended Uses |
|
|
| - Capybara semantic segmentation |
| - Computer vision research |
| - Segmentation benchmarking |
| - Real/synthetic training experiments |
| - Robustness and domain-gap studies involving animal segmentation |
|
|
| ## Limitations |
|
|
| - The dataset is focused on capybaras and may not generalize to other animals or object categories. |
| - Synthetic images may have a domain gap relative to real-world imagery. |
| - Data sources and visual distributions may contain biases. |
| - Users should validate generalization for their own deployment or research setting. |
|
|
| ## License / Redistribution |
|
|
| License and redistribution terms are being finalized before public release. |
|
|
| The final dataset license and original image redistribution status must be confirmed by the publisher before making the Hugging Face repository public. |
|
|
