Document the prepared classroom dataset

README.md

@@ -36,4 +36,126 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+task_categories:
+- image-classification
+pretty_name: '24-679 (Fall 2026): Stairs Verse Non-Stair Images'
+tags:
+- image
+- education
+- 24-679
+- data-augmentation
 ---
+
+# 24-679 (Fall 2026): Stairs and Non-Stair Images
+
+ArinRoths/Photo_Dataset
+
+Photos of stairs and non-stairs scenes, prepared as square RGB images with multiple separately
+generated training variants. The goal of this dataset is to classify whether or not stairs are
+present in an image.
+
+## Source and task
+
+The original dataset contains 32 images that I collected and organized into `stairs` and
+`non_stairs` folders. There are 16 original stairs images and 16 original non-stairs images.
+The folder containing each image determines its label. **0 = non-stairs; 1 = stairs.**
+
+Course: 24-679, Fall 2026, Carnegie Mellon University. Repository maintainer: the account shown above.
+
+## Fields
+
+| Field | Meaning and modeling role |
+|---|---|
+| `image` | Prepared RGB pixels; the model's image input. |
+| `label` | Classification target: 0 = non-stairs; 1 = stairs. |
+| `label_name` | Readable target description; exclude from model inputs. |
+| `source_id`, `parent_id` | Unique prepared/augmented example key and its original source key; provenance only. |
+| `augmentation`, `is_augmented` | Method identifier and random-augmentation flag; provenance only. |
+
+## Splits and original-source counts
+
+These counts are computed from the packaged splits for this run.
+
+| Split | Original rows | Synthetic rows | Total rows |
+|---|---:|---:|---:|
+| train | 22 | 352 | 374 |
+| validation | 5 | 0 | 5 |
+| test | 5 | 0 | 5 |
+
+Class counts include synthetic rows in training only.
+
+| Split | Label | Rows |
+|---|---:|---:|
+| train | 0 | 187 |
+| train | 1 | 187 |
+| validation | 0 | 2 |
+| validation | 1 | 3 |
+| test | 0 | 3 |
+| test | 1 | 2 |
+
+Requested holdout fraction: 30%; test receives 50% of that holdout.
+Small-sample rounding changes the realized proportions. The first split uses seed 24679, and the
+holdout split uses seed 24680.
+
+Split original image indices with class stratification before creating any random variants.
+Validation and test contain only unaugmented prepared parents. A source and its descendants never
+cross boundaries. The augmented rows are not split again.
+
+## Augmentation and preprocessing
+
+Prepared image size in this run: **224 × 224 RGB**. Padding color is **(128, 128, 128)**.
+
+All splits receive the same deterministic preparation: apply EXIF orientation, convert to RGB,
+resize while preserving aspect ratio, and pad to the configured square size. Working copies discard
+camera metadata; native source files remain separate. Downstream models still need their own
+checkpoint-specific input processing.
+
+Each original training source contributes its prepared parent and sixteen independent variants;
+transforms are not stacked. Four variants are created using each augmentation method: brightness,
+rotation, contrast, and Gaussian blur. The current notebook uses brightness factors 0.4–0.7 or
+1.4–2.0; rotation of 15–30 degrees in either direction on a fixed canvas; contrast factors
+0.35–0.65 or 1.5–2.2; and Gaussian blur. Lower/higher brightness and contrast ranges are chosen
+with equal probability. Rotation fills exposed corners with a color derived from the parent and
+can clip edges.
+
+The stored identifiers `mild_brightness`, `small_rotation`, `mild_contrast`, and
+`mild_gaussian_blur` refer to the four augmentation methods used in the notebook.
+
+| Training method | Stored rows |
+|---|---:|
+| `mild_brightness` | 88 |
+| `mild_contrast` | 88 |
+| `mild_gaussian_blur` | 88 |
+| `none` | 22 |
+| `small_rotation` | 88 |
+
+## Intended use and limitations
+
+This dataset is intended for image classification and for practicing image preparation,
+augmentation, and transfer learning. The original collection is small, with only 32 independent
+images. Backgrounds, lighting, camera angles, and differences between locations could affect the
+model instead of only the presence of stairs. Brightness and contrast changes can hide details,
+rotation can crop parts of an image, and blur can make stairs harder to see. The augmented images
+also do not add new independent scenes because they are all based on the original training images.
+The parent and variant galleries should be inspected to make sure the augmentations still preserve
+the correct label.
+
+## Privacy and licensing
+
+The packaged dataset does not include the original image filenames. The preparation workflow uses
+anonymous source IDs, and prepared image bytes do not retain camera metadata. The images were
+reviewed to avoid including faces, people, or other personally identifiable or sensitive
+information. No license was specified for this dataset.
+
+## Load and compare
+
+```python
+from datasets import load_dataset
+ds = load_dataset("ArinRoths/Photo_Dataset")
+# Train with ds["train"], choose settings with ds["validation"], then score ds["test"].
+```
+
+Use an account with access if repository visibility changes. For reproducible comparisons, record the
+dataset commit and model/environment versions. Regenerate this card with the preparation notebook after
+changing the data; its counts are calculated from the actual packaged splits. The YAML schema and split
+configuration are preserved from the upload.