Add dataset card

README.md

@@ -1,35 +1,110 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: split
-    dtype: string
-  - name: has_polyp
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 1152010120
-    num_examples: 799
-  - name: val
-    num_bytes: 142411134
-    num_examples: 100
-  - name: test
-    num_bytes: 144413008
-    num_examples: 100
-  download_size: 1438040809
-  dataset_size: 1438834262
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
+license: other
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - endoscopy
+  - colonoscopy
+  - polyp
+  - segmentation
+  - bkai
+size_categories:
+  - n<10K
 ---
+
+# BKAI-IGH NeoPolyp (binary, MedCLIPSeg mirror)
+
+Re-hosted mirror of the **BKAI-IGH NeoPolyp "Small"** subset (Lan et al.,
+2021), originally released through the
+[BKAI-IGH NeoPolyp Kaggle competition](https://www.kaggle.com/c/bkai-igh-neopolyp/),
+intended for use with [EasyMedSeg](https://github.com/).
+
+This mirror is rebuilt from the
+[`TahaKoleilat/MedCLIPSeg`](https://huggingface.co/datasets/TahaKoleilat/MedCLIPSeg)
+HF dataset's `BKAI.zip` (the only freely-fetchable HF rehost we found that
+ships the masks). We chose this source because the canonical Kaggle URL
+requires Kaggle competition-acceptance + an API token, which is awkward
+for downstream automation.
+
+## Composition
+
+| Split   | Images | With polyp |
+|---------|-------:|-----------:|
+| train   | 799    | (computed) |
+| val     | 100    | (computed) |
+| test    | 100    | (computed) |
+| **All** | **999**|            |
+
+Image dimensions: variable (~1280 × 950–1000 px), heterogeneous endoscopy
+frames in JPEG. The original Kaggle release contains 1,000 train (with
+masks) + 200 test (held-out masks); this mirror uses the 1,000 train pool
+re-split into 799/100/100. The 200-image canonical Kaggle test split with
+no public masks is **not** included.
+
+## Mask caveat — binary only
+
+The upstream MedCLIPSeg mirror saved the original
+3-channel RGB-coded semantic masks as **JPEG-compressed grayscale**.
+JPEG compression introduces boundary noise (we observed pixel values
+1–40 and 211–254 in addition to 0/255), and JPEG-on-label-map is
+inherently lossy.
+
+This mirror **thresholds at > 127** to recover a clean binary
+{0, 255} mask. **The 3-class
+(background / non-neoplastic polyp / neoplastic polyp) distinction in
+the original Kaggle PNGs is NOT recoverable from this source.**
+Use this mirror for **binary polyp segmentation** only. Pull the
+canonical Kaggle data directly if you need the 3-class formulation
+required to reproduce the NeoUNet / BlazeNeo benchmarks.
+
+## Schema
+
+| Column      | Type     | Description                                 |
+|-------------|----------|---------------------------------------------|
+| `image`     | `Image`  | Source RGB frame (PNG bytes, variable size) |
+| `mask`      | `Image`  | Binary mask (`L` mode, 0/255)               |
+| `image_id`  | `string` | 32-char hex stem from the source filename   |
+| `split`     | `string` | `train` / `val` / `test`                    |
+| `has_polyp` | `bool`   | `True` iff the mask contains any foreground |
+
+## License
+
+The original Kaggle release does not declare a public license; usage is
+implicitly governed by Kaggle competition rules ("research / academic use").
+The intermediate `TahaKoleilat/MedCLIPSeg` mirror redistributes under
+**CC-BY-NC-4.0** (mirror-imposed, not author-confirmed). Treat as
+**research / non-commercial only** until BKAI/IGH publishes a formal
+license.
+
+## Citation
+
+```bibtex
+@inproceedings{lan2021neounet,
+  title     = {{NeoUNet}: Towards Accurate Colon Polyp Segmentation
+               and Neoplasm Detection},
+  author    = {Lan, Phan Ngoc and An, Nguyen Sy and Hang, Dao Viet
+               and Long, Dao Van and Trung, Tran Quang
+               and Thuy, Nguyen Thi and Sang, Dinh Viet},
+  booktitle = {Advances in Visual Computing -- ISVC 2021},
+  series    = {Lecture Notes in Computer Science},
+  volume    = {13018},
+  pages     = {15--28},
+  publisher = {Springer},
+  year      = {2021},
+  doi       = {10.1007/978-3-030-90436-4_2}
+}
+
+@article{an2022blazeneo,
+  title   = {{BlazeNeo}: Blazing Fast Polyp Segmentation
+             and Neoplasm Detection},
+  author  = {An, Nguyen Sy and Lan, Phan Ngoc and Hang, Dao Viet
+             and Long, Dao Van and Trung, Tran Quang
+             and Thuy, Nguyen Thi and Sang, Dinh Viet},
+  journal = {IEEE Access},
+  volume  = {10},
+  pages   = {43669--43684},
+  year    = {2022},
+  doi     = {10.1109/ACCESS.2022.3168693}
+}
+```