Update README with LanceDB examples

README.md

@@ -3,6 +3,7 @@ license: bsd-3-clause
 task_categories:
 - image-segmentation
 - image-feature-extraction
+- lance
 language:
 - en
 tags:
@@ -18,14 +19,21 @@ size_categories:
 ---
 # ADE20K (Lance Format)
 
-Lance-formatted version of the full [ADE20K scene parsing benchmark](https://groups.csail.mit.edu/vision/datasets/ADE20K/) (sourced from [`1aurent/ADE20K`](https://huggingface.co/datasets/1aurent/ADE20K)) — **27,574 scene images** with semantic and instance segmentation maps, scene labels, and per-object metadata, all stored inline.
+A Lance-formatted version of the full [ADE20K scene parsing benchmark](https://groups.csail.mit.edu/vision/datasets/ADE20K/), sourced from [`1aurent/ADE20K`](https://huggingface.co/datasets/1aurent/ADE20K). Each row is one scene image with its inline JPEG bytes, a per-pixel semantic segmentation map encoded as PNG bytes, an optional instance map, scene class labels, the full per-polygon object-name list, an OpenCLIP image embedding, and pre-built indices — all available directly from the Hub at `hf://datasets/lance-format/ade20k-lance/data`.
+
+## Key features
+
+- **Inline image and segmentation bytes** — both the JPEG image and the RGB-encoded PNG segmentation map ride on the same row, so an annotated example is a single row read with no sidecar files.
+- **Per-polygon object metadata** — `object_names` keeps the full list (one entry per annotated polygon), `objects_present` is the deduped set used for class-presence filters, and `num_objects` is precomputed.
+- **CLIP image embeddings** (`image_emb`, OpenCLIP ViT-B/32, 512-d, cosine-normalized) for visual retrieval over scenes.
+- **Indices shipped on disk** — `IVF_PQ` on `image_emb`, `BTREE` on `num_objects`, and `LABEL_LIST` on `objects_present` for fast `array_has_any` / `array_has_all` predicates.
 
 ## Splits
 
 | Split | Rows |
 |-------|------|
 | `train.lance`      | 25,574 |
-| `validation.lance` | 2,000 |
+| `validation.lance` | 2,000  |
 
 ## Schema
 
@@ -36,70 +44,104 @@ Lance-formatted version of the full [ADE20K scene parsing benchmark](https://gro
 | `segmentation` | `large_binary` | Inline PNG bytes — semantic segmentation map (RGB encoding per ADE20K spec) |
 | `instance` | `large_binary?` | Inline PNG bytes — instance map; null if not provided |
 | `filename` | `string` | ADE20K relative filename |
-| `scene` | `list<string>` | Scene labels (e.g. `["bathroom"]`) |
-| `object_names` | `list<string>` | Names of all annotated objects (one entry per polygon) |
+| `scene` | `list<string>` | Scene class labels (e.g. `["bathroom"]`) |
+| `object_names` | `list<string>` | Per-polygon object names (one entry per polygon, not deduped) |
 | `objects_present` | `list<string>` | Deduped object names — feeds the `LABEL_LIST` index |
 | `num_objects` | `int32` | Number of annotated objects |
-| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP `ViT-B-32` image embedding (cosine-normalized) |
+| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP ViT-B/32 image embedding (cosine-normalized) |
 
 ## Pre-built indices
 
-- `IVF_PQ` on `image_emb` — `metric=cosine`
-- `BTREE` on `num_objects`
-- `LABEL_LIST` on `objects_present` — supports `array_has_any` / `array_has_all`
+- `IVF_PQ` on `image_emb` — vector similarity search (cosine)
+- `BTREE` on `num_objects` — fast range filters on scene complexity
+- `LABEL_LIST` on `objects_present` — supports `array_has_any` / `array_has_all` for class-presence filtering
+
+## Why Lance?
+
+1. **Blazing Fast Random Access**: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
+2. **Native Multimodal Support**: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
+3. **Native Index Support**: Lance comes with fast, on-disk, scalable vector and FTS indexes that sit right alongside the dataset on the Hub, so you can share not only your data but also your embeddings and indexes without your users needing to recompute them.
+4. **Efficient Data Evolution**: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
+5. **Versatile Querying**: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
+6. **Data Versioning**: Every mutation commits a new version; previous versions remain intact on disk. Tags pin a snapshot by name, so retrieval systems and training runs can reproduce against an exact slice of history.
+
+## Load with `datasets.load_dataset`
 
-## Quick start
+You can load Lance datasets via the standard HuggingFace `datasets` interface, suitable when your pipeline already speaks `Dataset` / `IterableDataset` or you want a quick streaming sample without installing anything Lance-specific.
 
 ```python
-import lance
+import datasets
 
-ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
-print(ds.count_rows(), ds.schema.names, ds.list_indices())
+hf_ds = datasets.load_dataset("lance-format/ade20k-lance", split="validation", streaming=True)
+for row in hf_ds.take(3):
+    print(row["filename"], row["scene"], row["num_objects"])
 ```
 
 ## Load with LanceDB
 
-These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.
+LanceDB is the embedded retrieval library built on top of the Lance format ([docs](https://lancedb.com/docs)), and is the interface most users interact with. It wraps the dataset as a queryable table with search and filter builders, and is the entry point used by the Search, Curate, Evolve, Versioning, and Materialize-a-subset sections below.
 
 ```python
 import lancedb
 
 db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
 tbl = db.open_table("validation")
-print(f"LanceDB table opened with {len(tbl)} scene images")
+print(len(tbl))
 ```
 
-## Read an image with its segmentation
+## Load with Lance
+
+`pylance` is the Python binding for the Lance format and works directly with the format's lower-level APIs. Reach for it when you want to inspect dataset internals — schema, scanner, fragments, the list of pre-built indices.
 
 ```python
-import io
 import lance
-from PIL import Image
 
 ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
-row = ds.take([0], columns=["image", "segmentation", "scene", "objects_present"]).to_pylist()[0]
-
-Image.open(io.BytesIO(row["image"])).save("img.jpg")
-Image.open(io.BytesIO(row["segmentation"])).save("seg.png")
-print("scene:", row["scene"])
-print("objects:", row["objects_present"][:10])
+print(ds.count_rows(), ds.schema.names)
+print(ds.list_indices())
 ```
 
-## Filter by scene / objects
+> **Tip — for production use, download locally first.** Streaming from the Hub works for exploration, but heavy random access, ANN search, and any mutation are far faster against a local copy:
+> ```bash
+> hf download lance-format/ade20k-lance --repo-type dataset --local-dir ./ade20k-lance
+> ```
+> Then point Lance or LanceDB at `./ade20k-lance/data`.
+
+## Search
+
+The bundled `IVF_PQ` index on `image_emb` makes approximate-nearest-neighbor scene retrieval a single call. In production you would encode a query image through the same OpenCLIP ViT-B/32 model used at ingest and pass the resulting 512-d vector to `tbl.search(...)`. The example below uses the embedding stored on row 42 as a runnable stand-in, so the snippet works without loading any model.
 
 ```python
-import lance
-ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
+import lancedb
+
+db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
+tbl = db.open_table("validation")
 
-# Indoor scenes containing both a bed and a window.
-rows = ds.scanner(
-    filter="array_has_all(objects_present, ['bed', 'window'])",
-    columns=["filename", "scene"],
-    limit=10,
-).to_table().to_pylist()
+seed = (
+    tbl.search()
+    .select(["image_emb", "filename", "scene"])
+    .limit(1)
+    .offset(42)
+    .to_list()[0]
+)
+
+hits = (
+    tbl.search(seed["image_emb"])
+    .metric("cosine")
+    .select(["filename", "scene", "objects_present"])
+    .limit(10)
+    .to_list()
+)
+print("query scene:", seed["scene"])
+for r in hits:
+    print(f"  {r['filename']}  scene={r['scene']}  objs={r['objects_present'][:5]}")
 ```
 
-### Filter with LanceDB
+Because the embeddings are cosine-normalized, the first hit will typically be the source image itself — a useful sanity check. Tune `nprobes` and `refine_factor` to trade recall against latency for your workload.
+
+## Curate
+
+Curation for a semantic-segmentation workflow usually means picking scenes that contain specific classes, possibly bounded by complexity. The `LABEL_LIST` index on `objects_present` makes class-presence predicates trivial, and Lance evaluates them inside the same scan as a structural filter on `num_objects`. The bounded `.limit(500)` keeps the result small and inspectable, and the `segmentation` blob is left out of the projection so the candidate scan is dominated by metadata, not PNG bytes.
 
 ```python
 import lancedb
@@ -107,33 +149,81 @@ import lancedb
 db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
 tbl = db.open_table("validation")
 
-rows = (
+candidates = (
     tbl.search()
-    .where("array_has_all(objects_present, ['bed', 'window'])")
-    .select(["filename", "scene"])
-    .limit(10)
+    .where(
+        "array_has_all(objects_present, ['bed', 'window']) AND num_objects >= 8",
+        prefilter=True,
+    )
+    .select(["id", "filename", "scene", "objects_present", "num_objects"])
+    .limit(500)
     .to_list()
 )
+print(f"{len(candidates)} candidates; first scene: {candidates[0]['scene']}")
 ```
 
-## Visual similarity search
+The result is a plain list of dictionaries, ready to inspect, persist as a manifest of `id`s, or feed into the Evolve and Train workflows below. Swapping `array_has_all` for `array_has_any` widens the recall; replacing the structural predicate with `num_objects BETWEEN 3 AND 6` selects simpler scenes for an ablation slice.
+
+## Evolve
+
+Lance stores each column independently, so a new column can be appended without rewriting the existing data. The lightest form is a SQL expression: derive the new column from columns that already exist, and Lance computes it once and persists it. The example below adds a `has_person` flag and a `scene_label` string pulled out of the `scene` list, either of which can then be used directly in `where` clauses without recomputing the predicate on every query.
+
+> **Note:** Mutations require a local copy of the dataset, since the Hub mount is read-only. See the Materialize-a-subset section at the end of this card for a streaming pattern that downloads only the rows and columns you need, or use `hf download` to pull the full corpus first.
+
+```python
+import lancedb
+
+db = lancedb.connect("./ade20k-lance/data")  # local copy required for writes
+tbl = db.open_table("validation")
+
+tbl.add_columns({
+    "has_person": "array_has_any(objects_present, ['person'])",
+    "scene_label": "element_at(scene, 1)",
+    "complexity_bucket": "CASE WHEN num_objects < 5 THEN 'sparse' "
+                        "WHEN num_objects < 15 THEN 'medium' ELSE 'dense' END",
+})
+```
+
+If the values you want to attach already live in another table (offline panoptic ids, predictions from a baseline segmenter, a second-pass embedding), merge them in by joining on `id`:
 
 ```python
-import lance
 import pyarrow as pa
 
-ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
-emb_field = ds.schema.field("image_emb")
-ref = ds.take([0], columns=["image_emb"]).to_pylist()[0]["image_emb"]
-query = pa.array([ref], type=emb_field.type)
-
-neighbors = ds.scanner(
-    nearest={"column": "image_emb", "q": query[0], "k": 5},
-    columns=["filename", "scene"],
-).to_table().to_pylist()
+predictions = pa.table({
+    "id": pa.array([0, 1, 2], type=pa.int64()),
+    "baseline_miou": pa.array([0.41, 0.55, 0.62]),
+})
+tbl.merge(predictions, on="id")
 ```
 
-### LanceDB visual similarity search
+The original columns and indices are untouched, so existing code that does not reference the new columns continues to work unchanged. New columns become visible to every reader as soon as the operation commits. For column values that require a Python computation (e.g., re-running a segmentation model over the image bytes), Lance provides a batch-UDF API — see the [Lance data evolution docs](https://lance.org/guide/data_evolution/).
+
+## Train
+
+Projection lets a training loop read only the columns each step actually needs. LanceDB tables expose this through `Permutation.identity(tbl).select_columns([...])`, which plugs straight into the standard `torch.utils.data.DataLoader` so prefetching, shuffling, and batching behave as in any PyTorch pipeline. For a semantic-segmentation run, project the JPEG bytes and the segmentation PNG bytes; both are decoded inside the training step. Columns added in the Evolve section above cost nothing per batch until they are explicitly projected.
+
+```python
+import lancedb
+from lancedb.permutation import Permutation
+from torch.utils.data import DataLoader
+
+db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
+tbl = db.open_table("train")
+
+train_ds = Permutation.identity(tbl).select_columns(["image", "segmentation"])
+loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=4)
+
+for batch in loader:
+    # batch carries only the JPEG and PNG byte columns; decode both,
+    # remap the ADE20K RGB-encoded mask to class ids, forward, loss...
+    ...
+```
+
+Switching feature sets is a configuration change: passing `["image_emb", "objects_present"]` to `select_columns(...)` on the next run skips JPEG and PNG decoding entirely and reads only the cached 512-d vectors plus the deduped class list, which is the right shape for training a lightweight scene classifier or a class-presence probe on top of frozen features.
+
+## Versioning
+
+Every mutation to a Lance dataset, whether it adds a column, merges labels, or builds an index, commits a new version. Previous versions remain intact on disk. You can list versions and inspect the history directly from the Hub copy; creating new tags requires a local copy since tags are writes.
 
 ```python
 import lancedb
@@ -141,23 +231,51 @@ import lancedb
 db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
 tbl = db.open_table("validation")
 
-ref = tbl.search().limit(1).select(["image_emb"]).to_list()[0]
-query_embedding = ref["image_emb"]
+print("Current version:", tbl.version)
+print("History:", tbl.list_versions())
+print("Tags:", tbl.tags.list())
+```
 
-results = (
-    tbl.search(query_embedding)
-    .metric("cosine")
-    .select(["filename", "scene"])
-    .limit(5)
-    .to_list()
-)
+Once you have a local copy, tag a version for reproducibility:
+
+```python
+local_db = lancedb.connect("./ade20k-lance/data")
+local_tbl = local_db.open_table("validation")
+local_tbl.tags.create("segmenter-baseline-v1", local_tbl.version)
 ```
 
-## Why Lance?
+A tagged version can be opened by name, or any version reopened by its number, against either the Hub copy or a local one:
+
+```python
+tbl_v1 = db.open_table("validation", version="segmenter-baseline-v1")
+tbl_v5 = db.open_table("validation", version=5)
+```
+
+Pinning supports two workflows. A serving pipeline locked to `segmenter-baseline-v1` keeps reading the exact same segmentation maps and class lists while the dataset evolves in parallel; newly merged predictions or evolved columns do not change what the tag resolves to. A training experiment pinned to the same tag can be rerun later against the exact same images, so changes in mIoU reflect model changes rather than data drift. Neither workflow needs shadow copies or external manifest tracking.
+
+## Materialize a subset
+
+Reads from the Hub are lazy, so exploratory queries only transfer the columns and row groups they touch. Mutating operations (Evolve, tag creation) need a writable backing store, and a training loop benefits from a local copy with fast random access. Both can be served by a subset of the dataset rather than the full split. The pattern is to stream a filtered query through `.to_batches()` into a new local table; only the projected columns and matching row groups cross the wire, and the bytes never fully materialize in Python memory.
+
+```python
+import lancedb
+
+remote_db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
+remote_tbl = remote_db.open_table("train")
+
+batches = (
+    remote_tbl.search()
+    .where("array_has_any(objects_present, ['bed', 'sofa', 'chair']) AND num_objects >= 5")
+    .select(["id", "image", "segmentation", "filename", "scene",
+             "objects_present", "num_objects", "image_emb"])
+    .to_batches()
+)
+
+local_db = lancedb.connect("./ade20k-indoor-subset")
+local_db.create_table("train", batches)
+```
 
-- One dataset for images + segmentation + instance + scene + objects + embeddings + indices — no folder of paired files.
-- On-disk vector and label-list indices live next to the data, so search works on local copies and on the Hub.
-- Schema evolution: add columns (panoptic ids, fresh embeddings, model predictions) without rewriting the data.
+The resulting `./ade20k-indoor-subset` is a first-class LanceDB database. Every snippet in the Evolve, Train, and Versioning sections above works against it by swapping `hf://datasets/lance-format/ade20k-lance/data` for `./ade20k-indoor-subset`.
 
 ## Source & license