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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-classification
+tags:
+- precision-at-scale
+- birds
+- arxiv:2407.03463
+pretty_name: PaS-B
+configs:
+- config_name: web
+  data_files: "PaS-B-web.parquet"
+- config_name: synthetic
+  data_files: "PaS-B-synthetic-*.parquet"
+dataset_info:
+- config_name: web
+  features:
+      - name: image_name
+        dtype: string
+      - name: url
+        dtype: string
+- config_name: synthetic
+  features:
+      - name: image
+        dtype: image
+      - name: image_name
+        dtype: string
+---
+
+# PaS-B
+
+Birds-domain dataset used to train the `PaS-B` checkpoint from
+the *Precision at Scale* paper (1.2M images).
+
+## Paper
+
+- **Paper (Pattern Recognition, 2026):** https://doi.org/10.1016/j.patcog.2025.112236
+- **Official code:** https://github.com/jesusmolrdv/Precision-at-Scale
+
+```bibtex
+@article{rodriguezdevera2026precision,
+  title   = {Precision at scale: Domain-specific datasets on-demand},
+  author  = {Rodr{\'i}guez-de-Vera, Jes{\'u}s M. and Estepa, Imanol G. and Saras{\'u}a, Ignacio and Nagarajan, Bhalaji and Radeva, Petia},
+  journal = {Pattern Recognition},
+  volume  = {171},
+  pages   = {112236},
+  year    = {2026},
+  publisher = {Elsevier},
+  doi     = {10.1016/j.patcog.2025.112236}
+}
+```
+
+<details>
+<summary>arXiv preprint</summary>
+
+https://arxiv.org/abs/2407.03463
+
+</details>
+
+## Composition
+
+Published as two splits:
+
+- **`web`**: 358,292 rows. URL-only, no
+  image bytes hosted. Reconstruct locally with
+  [`img2dataset`](https://github.com/rom1504/img2dataset). Obtained directly
+  from Re-LAION-5B.
+- **`synthetic`**: 856,467 rows. Actual image bytes, generated
+  with [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
+  
+
+## Data Owner Opt-Out
+
+The `web` split contains only URLs pointing at third-party web images (no
+image bytes), sourced from [Re-LAION-5B](https://laion.ai/blog/relaion-5b/).
+If you own an image referenced by a URL in this split and want it removed:
+
+- **From the upstream index**: submit a removal request via
+  [Spawning's haveibeentrained.com](https://haveibeentrained.com), the
+  official opt-out mechanism LAION uses to build each Re-LAION-5B revision.
+- **From this specific redistribution**: since this split is a snapshot
+  taken at a point in time, an upstream opt-out registered afterward won't
+  automatically propagate here. Open an issue on the
+  [official repository](https://github.com/jesusmolrdv/Precision-at-Scale/issues) with the URL(s) or
+  `image_name`(s) in question, and they will be removed from future
+  releases of this dataset.
+
+## Usage
+
+Load the `synthetic` split directly (image bytes are hosted):
+
+```python
+from datasets import load_dataset
+
+synthetic = load_dataset("jesusmolrdv/pas-b", "synthetic", split="train")
+```
+
+The `web` split has no hosted image bytes, only URLs. Reconstruct it locally with
+[`img2dataset`](https://github.com/rom1504/img2dataset):
+
+```python
+from datasets import load_dataset
+
+web = load_dataset("jesusmolrdv/pas-b", "web", split="train").to_pandas()
+web[["url", "image_name"]].to_parquet("PaS-B-web-urls.parquet")
+```
+
+```sh
+img2dataset --url_list PaS-B-web-urls.parquet --input_format parquet \
+    --url_col url --save_additional_columns '["image_name"]' \
+    --output_folder PaS-B_web_images --output_format webdataset
+```