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README.md

@@ -8,8 +8,8 @@ tags:
 - wildfake
 dataset_info:
   features:
-  - name: image
-    dtype: image
+  - name: image_bytes
+    dtype: binary
   - name: split
     dtype: string
   - name: group
@@ -32,10 +32,7 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 10562437
-    num_examples: 200
-  download_size: 10544758
-  dataset_size: 10562437
+    num_examples: 30000
 configs:
 - config_name: default
   data_files:
@@ -91,42 +88,55 @@ Total: **30,000 images** (19,500 fake / 10,500 real).
 ## Files
 
 ```
-real/<source>/*.png       10,500 real images, 6 sources
-fake/<generator>/*.png    19,500 fake images, 26 generators
-manifest.csv / .json      one row per image: which archive/path it came
-                          from inside WildFake, category, split, resolution
-transform_plan.csv / .json  a fixed, deterministic per-image assignment of
-                          one of 14 corruption conditions (JPEG/blur/resize/
-                          noise/color-jitter/center-crop at various
-                          severities) — see below
+data/train-*.parquet         the dataset itself — 8 shards, one row per
+                              image (see columns below)
+manifest.csv / .json         same per-image metadata as the parquet's
+                              columns, as plain CSV/JSON, for anyone who
+                              wants it without going through `datasets`
+transform_plan.csv / .json   the per-image condition assignment on its own
+                              (see below) — a subset of what's in the parquet
 ```
 
-Every image is a PNG regardless of its original format inside WildFake (many
-of the fakes are natively PNG; reals are often JPEG) — deliberately not
-JPEG-recompressed at sampling time, so any JPEG compression applied during
-evaluation is the only lossy step, applied once, consistently.
-
-### `manifest.csv` columns
-`split` (real/fake), `group` (generator or real-source name), `category`
-(GAN_based/Diffusion_based/Other_based/Real), `source_zip` (which WildFake
-archive), `source_path` (path inside that archive), `local_path` (path in
-this dataset), `width`, `height`.
-
-### `transform_plan.csv` columns
-`local_path`, `group`, `split`, `category`, `width`, `height`, `area`
-(width×height), `condition` (e.g. "JPEG q=30", "Blur sigma=1.0"), `family`
-(JPEG/Blur/Resize/Noise/ColorJitter/CenterCrop), `order` (always
-`transform_first` — the condition is applied to the image, then a final
-standard q=96 JPEG pass is applied last, standardizing the final encoding
-every image ends up in regardless of which condition it drew).
-
-**How the assignment was made** (no randomness, fully reproducible from
-`manifest.csv` alone): within each group independently, images are sorted
-by pixel area ascending, then cycled round-robin through the 14 conditions.
-Sorting before cycling stratifies the assignment across resolution (any 14
-consecutive area-sorted images cover every condition once, so no condition
-is systematically biased toward small or large images); cycling makes it
-uniform (every condition gets within 1 image of an even split per group).
+### Columns (`data/train-*.parquet`, and equivalently `manifest.csv` joined
+with `transform_plan.csv` by row)
+
+`image_bytes` (binary — the original image file's bytes, unmodified: a PNG
+regardless of the image's format inside WildFake, since many of the fakes
+are natively PNG but reals are often JPEG; not JPEG-recompressed at sampling
+time, so any JPEG compression applied during evaluation is the only lossy
+step, applied once, consistently), `split` (real/fake), `group` (generator
+or real-source name), `category` (GAN_based/Diffusion_based/Other_based/
+Real), `source_zip` / `source_path` (which WildFake archive and path inside
+it this image came from), `width`, `height`, `condition` (e.g. "JPEG q=30",
+"Blur sigma=1.0" — see "Transform assignment" below), `family` (JPEG/Blur/
+Resize/Noise/ColorJitter/CenterCrop), `order` (always `transform_first` —
+the condition is applied to the image, then a final standard q=96 JPEG pass
+is applied last, standardizing the final encoding every image ends up in
+regardless of which condition it drew).
+
+**Why `image_bytes` is plain binary, not the `datasets` library's `Image`
+feature type**: `Image` hit two separate bugs on this dataset (a crash in
+`push_to_hub`'s internal shard-embedding step, and — after working around
+that — silently-empty bytes from `to_parquet()` skipping the same embedding
+step instead of erroring). Plain bytes have no such embedding step to break.
+Decode with:
+```python
+from PIL import Image
+import io
+img = Image.open(io.BytesIO(row["image_bytes"]))
+```
+
+### Transform assignment
+
+**How it was made** (no randomness, fully reproducible from the manifest
+alone): within each group independently, images are sorted by pixel area
+ascending, then cycled round-robin through 14 conditions (JPEG q90/70/50/30,
+Gaussian blur σ0.5/1.0/2.0, resize 0.5x/0.25x, Gaussian noise σ.02/.05/.10,
+color jitter, center-crop). Sorting before cycling stratifies the assignment
+across resolution (any 14 consecutive area-sorted images cover every
+condition once, so no condition is systematically biased toward small or
+large images); cycling makes it uniform (every condition gets within 1
+image of an even split per group).
 
 ## How this was built
 
@@ -136,6 +146,6 @@ reading only each archive's central directory (a listing) plus the exact
 compressed bytes of the specific images sampled, never downloading an
 archive in full. First-N selection per generator/source in each archive's
 own listing order (no random sampling). See
-`data_prep/build_wildfake_eval.py` and
-`data_prep/build_wildfake_transform_plan.py` in the GitHub repo above for
-the exact code.
+`data_prep/build_wildfake_eval.py`, `data_prep/build_wildfake_transform_plan.py`,
+and `data_prep/push_wildfake_to_hf.py` in the GitHub repo above for the
+exact code.