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---
pretty_name: Baby Yoda LEGO Presence (224×224)
tags: [image, classification, augmentation, education]
license: cc-by-4.0
task_categories: [image-classification]
---
# Baby Yoda LEGO Presence (224×224)
Binary image dataset indicating whether an image **contains a Baby Yoda LEGO** figure.
## Composition & Collection
- **Creators:** Student-captured photos by the dataset author for an academic assignment.
- **Subjects:** Desktop/object scenes that may contain a Baby Yoda LEGO figure.
- **Original count:** {orig_n} images (≥30 required).
- **Resolution / format:** Center-cropped square, resized to **224×224**, saved as JPEG.
- **Privacy:** No faces or personally identifiable information (PII) included.
## Labels
Binary target from folder names:
- `has_baby_yoda` (label = 0)
- `no_baby_yoda` (label = 1)
## Preprocessing
1. Decode `.heic/.jpg/.jpeg/.png` (HEIC via `pillow-heif`).
2. Center-crop to square using `min(width, height)`.
3. Resize to **224×224** with bilinear interpolation.
4. Save as JPEG (quality≈92).
## Augmentation (label-preserving)
Performed offline to create the `augmented` split (no generative models):
- `RandomResizedCrop(size=224, scale=(0.7, 1.0), ratio=(0.75, 1.33))`
- `RandomHorizontalFlip(p=0.5)`
- `RandomVerticalFlip(p=0.1)`
- `RandomRotation(±15°, bilinear)`
- `ColorJitter(brightness=0.2, contrast=0.2, saturation=0.15, hue=0.05)`
- `RandomErasing(p=0.2, scale=(0.02, 0.08), ratio=(0.3, 3.3))`
These transformations do not change whether Baby Yoda is present.
## Splits
- `original`: {orig_n} resized images (224×224)
- `augmented`: {aug_n} synthetic, label-preserving variants (≥300 total)
## Intended Use / Limits
- **Use:** Classroom demos, practice with CV pipelines, small experiments.
- **Not for:** Benchmarks or production systems (limited scope/diversity).
- **Caveats:** Background/lighting vary; class balance may be modest.
## Ethical Notes
- No people or PII.
- Images are student-created and used for coursework.
## Licensing
- **CC-BY-4.0** for the dataset and documentation.
## AI Usage Disclosure
Code was refactored from in class image dataset example, GenAI was used to assist with refactoring and comments
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