Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/train.lance/_indices/5e6cb445-cf6f-450d-b8a8-0c8ceb1d8e4d/auxiliary.idx filter=lfs diff=lfs merge=lfs -text
+data/train.lance/data/10000101011101100100001155503d4baebc977b5d2f773ddb.lance filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,81 @@
+---
+license: cc-by-sa-4.0
+task_categories:
+- image-classification
+- image-feature-extraction
+language:
+- en
+tags:
+- oxford-pets
+- fine-grained
+- pets
+- lance
+- clip-embeddings
+pretty_name: oxford-pets-lance
+size_categories:
+- 1K<n<10K
+---
+# Oxford-IIIT Pet (Lance Format)
+
+Lance-formatted version of the [Oxford-IIIT Pet dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) — 7,390 cat & dog photos across 37 breeds — sourced from [`pcuenq/oxford-pets`](https://huggingface.co/datasets/pcuenq/oxford-pets).
+
+## Schema
+
+| Column | Type | Notes |
+|---|---|---|
+| `id` | `int64` | Row index |
+| `image` | `large_binary` | Inline JPEG bytes |
+| `label_name` | `string` | One of 37 breeds, underscore-spaced (`british_shorthair`, `golden_retriever`, …) |
+| `is_dog` | `bool` | `true` for dog breeds, `false` for cat breeds |
+| `path` | `string?` | Original filename in the source dataset |
+| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP `ViT-B-32` embedding (cosine-normalized) |
+
+## Pre-built indices
+
+- `IVF_PQ` on `image_emb` — `metric=cosine`
+- `BITMAP` on `label_name` and `is_dog`
+
+## Quick start
+
+```python
+import lance
+ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
+print(ds.count_rows(), ds.schema.names, ds.list_indices())
+```
+
+## Filter — only dogs, only golden retrievers, etc.
+
+```python
+import lance
+ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
+dogs = ds.scanner(filter="is_dog = true", columns=["label_name"], limit=5).to_table()
+goldens = ds.scanner(filter="label_name = 'golden_retriever'", columns=["id"], limit=5).to_table()
+```
+
+## Visual similarity search
+
+```python
+import lance, pyarrow as pa
+ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
+emb_field = ds.schema.field("image_emb")
+ref = ds.take([0], columns=["image_emb", "label_name"]).to_pylist()[0]
+neighbors = ds.scanner(
+    nearest={"column": "image_emb", "q": pa.array([ref["image_emb"]], type=emb_field.type)[0], "k": 5},
+    columns=["id", "label_name"],
+).to_table().to_pylist()
+```
+
+## Source & license
+
+Converted from [`pcuenq/oxford-pets`](https://huggingface.co/datasets/pcuenq/oxford-pets). Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+
+## Citation
+
+```
+@inproceedings{parkhi2012cats,
+  title={Cats and Dogs},
+  author={Parkhi, Omkar M. and Vedaldi, Andrea and Zisserman, Andrew and Jawahar, C. V.},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2012}
+}
+```

data/train.lance/_indices/5e6cb445-cf6f-450d-b8a8-0c8ceb1d8e4d/auxiliary.idx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cf1a810bb16ffdab700ab6e3f9f38aff8b82dafb41dc3f286228fc9c1305aa0
+size 1012804

data/train.lance/data/10000101011101100100001155503d4baebc977b5d2f773ddb.lance

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b35f46155cd71fa6a5c336aa83ec2ccef983ae6ada77236cb3dc9ef29b2b602
+size 350299756