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README.md

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----
-license: apache-2.0
----
+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: PhillMagine v2
+size_categories:
+- n<1K
+task_categories:
+- text-to-image
+- image-to-text
+tags:
+- text-to-image
+- diffusion
+- training-data
+- captions
+- synthetic
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: prompt
+    dtype: string
+  - name: label
+    dtype: string
+  - name: has_text_elements
+    dtype: string
+  - name: source
+    dtype: string
+  - name: split
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 288
+  - name: test
+    num_examples: 32
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: test
+    path: test.parquet
+---
+
+# PhillMagine v2
+
+A small, curated text-to-image training dataset combining the original
+**PhillMagine** corpus with Gemini-synthesized samples, used to train a
+universal text-conditioning adapter for diffusion models.
+
+**Total: 320 rows** (288 train / 32 test).
+
+## Sources
+
+- **phillmagine120**: 120 rows
+- **gemini_synth_v1**: 200 rows
+
+## Schema
+
+Each row contains:
+
+| field | type | description |
+|---|---|---|
+| `image` | image | the image itself (HF ImageFolder loads from `file_name`) |
+| `prompt` | string | natural-language image description |
+| `label` | string | short snake_case category tag |
+| `has_text_elements` | string | `"yes"` if the image contains readable text, else `"no"` |
+| `source` | string | which sub-corpus this row came from |
+| `split` | string | `"train"` or `"test"` |
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+# from the Hub
+ds = load_dataset("ayjays132/PhillMagine-v2")
+print(ds["train"][0])
+
+# or from a local clone
+ds = load_dataset("imagefolder", data_dir="data/phillmagine_v2")
+```
+
+## Splits
+
+Split assignment is deterministic: hash(`split_seed`=1337 + prompt)
+→ stable across rebuilds, so adding new rows later only affects new rows.
+
+## Labels
+
+229 unique label tags, including: abstract, abstract_pattern, air_transport, airport_signs, alarm_clock_diagram, alien_sunset_illustration, ancient_architecture, animals, antique_bookshelf_close, architecture_diagram, art_deco_bedroom_illustration, art_deco_bedroom_rendering, art_supplies, athletic_sneakers_illustration, bags_types, baked_bread, bakery_breads, baking_ingredients, bald_eagle_photographic_labeled, barnyard_scene, basketball_sneakers_illustration, bathroom_toiletries_vector, berries_fresh, beverages, bird_anatomy, birds_perched, blue_tit_painterly_labeled, board_game_product_photo, board_games, books_lamp_illustration
+... (and more)
+
+## Build provenance
+
+- Built on 2026-05-21 with `scripts/build_phillmagine_v2.py`.
+- Image format: `jpg` @ q=92.
+- Target size: 512.
+- Test ratio: 10%.

dataset_info.json

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+{
+  "name": "PhillMagine-v2",
+  "hf_repo_id": "ayjays132/PhillMagine-v2",
+  "version": "2.0.0",
+  "build_date": "2026-05-21",
+  "license": "cc-by-4.0",
+  "total_rows": 320,
+  "splits": {
+    "train": 288,
+    "test": 32
+  },
+  "sources": {
+    "phillmagine120": 120,
+    "gemini_synth_v1": 200
+  },
+  "image_format": "jpg",
+  "target_size": 512,
+  "fields": {
+    "file_name": "str \u2014 path to the image, relative to dataset root",
+    "prompt": "str \u2014 natural-language image description",
+    "label": "str \u2014 short snake_case category tag",
+    "has_text_elements": "str \u2014 'yes' if the image contains readable text, else 'no'",
+    "source": "str \u2014 original dataset this row came from",
+    "split": "str \u2014 'train' or 'test'"
+  }
+}