Make README more succinct

README.md

@@ -42,110 +42,36 @@ configs:
 
 # WildFake-Sample
 
-A 30,000-image curated sample drawn from **WildFake** (Hao et al., AAAI 2025,
+A 30,000-image sample of **WildFake** (Hao et al., AAAI 2025,
 [arXiv:2402.11843](https://arxiv.org/abs/2402.11843);
 [original dataset](https://modelscope.cn/datasets/hy2628982280/WildFake)),
-built for evaluating AI-generated-image detectors' generalization to
-generators and real-image sources they were **not** trained on. Built for
-[`Buxt-Codes/AIGI-Detection`](https://github.com/Buxt-Codes/AIGI-Detection)
-(branch `LoRC-PC`) — see that repo's `HANDOFF.md` for the full evaluation
+covering generators and real-image sources outside DDA/SID — a held-out
+generalization slice, not a copy of the full ~3.6M-image dataset. All credit
+for the images goes to WildFake's original authors. Built for
+[Buxt-Codes/AIGI-Detection](https://github.com/Buxt-Codes/AIGI-Detection)
+(branch `LoRC-PC`) — see that repo's `HANDOFF.md` for the evaluation
 methodology and results.
 
-**This is a sample, not the full WildFake dataset** (which is ~3.6M images
-across the same generators). All credit for the underlying images and the
-dataset's design goes to WildFake's original authors — see their paper and
-dataset page for licensing/usage terms; this sample exists purely to give
-a fixed, reproducible evaluation slice across machines/checkpoints without
-re-downloading from the ~39 multi-GB source archives every time.
-
-## Why this composition
-
-The detector this sample was built to evaluate trained on SD-based fakes
-over COCO real images (DDA-Training-Set) and was previously evaluated on
-FLUX fakes (SID). This sample deliberately covers generators and real
-sources **outside** both of those: 26 fake generators spanning GAN-based,
-diffusion-based (non-SD and SD-family), and other architectures, plus 6 real
-sources. WildFake's own COCO real split is deliberately excluded here since
-the detector's training data is COCO-based — including it would risk
-sampling images already seen in training.
-
 ## Composition
 
-**Fake — 750 images per generator (19,500 total):**
-
-| Category | Generators |
-|---|---|
-| GAN-based | BigGAN, StyleGAN, StarGAN, DF-GAN, GALIP, GigaGAN |
-| Diffusion (non-SD) | ADM, DDPM, DDIM, Imagen, VQDM, DALL-E 2, DALL-E 3, Midjourney v4, Midjourney v5 |
-| Diffusion (SD family) | SDXL, Original SD, SD+ControlNet, SD+LoRA, SD+LyCORIS, Personalized SD (DreamBooth), Personalized SD (finetune) |
-| Other | MAGE, VQGAN, VQVAE, MAE |
-
-**Real — 1,750 images per source (10,500 total):** LAION-5B, ImageNet,
-LSUN-Church, FFHQ, AFHQ, CelebA-HQ.
-
-Total: **30,000 images** (19,500 fake / 10,500 real).
+- **Fake (19,500):** 750 × 26 generators — GANs (BigGAN, StyleGAN, StarGAN,
+  DF-GAN, GALIP, GigaGAN), non-SD diffusion (ADM, DDPM, DDIM, Imagen, VQDM,
+  DALL-E 2/3, Midjourney v4/v5), SD-family (SDXL, OriginalSD, ControlNet,
+  LoRA, LyCORIS, 2x personalized), other (MAGE, VQGAN, VQVAE, MAE).
+- **Real (10,500):** 1,750 × 6 sources — LAION-5B, ImageNet, LSUN-Church,
+  FFHQ, AFHQ, CelebA-HQ.
 
 ## Files
 
-```
-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
-```
-
-### 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).
+`data/train-*.parquet` — one row per image. `manifest.csv`/`.json` and
+`transform_plan.csv`/`.json` — the same metadata as plain CSV/JSON.
 
-## How this was built
+**Columns:** `image_bytes` (raw file bytes, undecoded — decode with
+`Image.open(io.BytesIO(row["image_bytes"]))`), `split`, `group`, `category`,
+`source_zip`/`source_path`, `width`/`height`, `condition` (one of 14
+transform-battery conditions, assigned round-robin per group, stratified by
+resolution), `family`, `order` (always `transform_first`: condition applied,
+then a final standardizing JPEG q=96 pass).
 
-Sampled from WildFake's ModelScope-hosted archives (several 51–53GB each,
-some bundling multiple generators together) via HTTP Range requests —
-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`, `data_prep/build_wildfake_transform_plan.py`,
-and `data_prep/push_wildfake_to_hf.py` in the GitHub repo above for the
-exact code.
+Full build methodology (HTTP range-request sampling, transform-assignment
+algorithm, source code): see the GitHub repo linked above.