--- license: cc-by-4.0 task_categories: - image-to-text - image-text-to-text - image-feature-extraction - lance language: - en tags: - coco - coco-captions - image-captioning - vision-language - lance - clip-embeddings pretty_name: coco-captions-2017-lance size_categories: - 10K The 2017 train split (118 k images, ~18 GB of source JPEGs) is intentionally not bundled here because the `lmms-lab/COCO-Caption2017` redistribution does not include it. To extend with train, run `coco_captions_2017/dataprep.py` against your local COCO 2017 train mirror. ## Schema | Column | Type | Notes | |---|---|---| | `id` | `int64` | Row index within split (natural join key) | | `image` | `large_binary` | Inline JPEG bytes | | `image_id` | `string` | COCO image id | | `filename` | `string` | Original filename (e.g. `000000179765.jpg`) | | `captions` | `list` | All 5–7 captions for the image | | `caption` | `string` | First caption — canonical text used for FTS | | `image_emb` | `fixed_size_list` | CLIP image embedding (cosine-normalized) | | `text_emb` | `fixed_size_list` | CLIP text embedding of the canonical caption | ## Pre-built indices - `IVF_PQ` on `image_emb` — image-side vector search (cosine) - `IVF_PQ` on `text_emb` — text-side vector search (cosine) - `INVERTED` (FTS) on `caption` — keyword and hybrid search - `BTREE` on `image_id` — fast lookup by COCO image id ## 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` 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. ```python import datasets hf_ds = datasets.load_dataset("lance-format/coco-captions-2017-lance", split="val", streaming=True) for row in hf_ds.take(3): print(row["caption"]) ``` ## Load with LanceDB 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/coco-captions-2017-lance/data") tbl = db.open_table("val") print(len(tbl)) ``` ## 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 lance ds = lance.dataset("hf://datasets/lance-format/coco-captions-2017-lance/data/val.lance") print(ds.count_rows(), ds.schema.names) print(ds.list_indices()) ``` > **Tip — for production use, download locally first.** Streaming from the Hub works for exploration, but heavy random access and ANN search are far faster against a local copy: > ```bash > hf download lance-format/coco-captions-2017-lance --repo-type dataset --local-dir ./coco-captions-2017-lance > ``` > Then point Lance or LanceDB at `./coco-captions-2017-lance/data`. ## Search The bundled `IVF_PQ` index on `image_emb` makes cross-modal text→image retrieval a single call: encode a text query with the same CLIP model used at ingest (ViT-B/32, cosine-normalized), then pass the resulting 512-d vector to `tbl.search(...)` and target `image_emb`. The example below uses the `text_emb` already stored in row 42 as a runnable stand-in for "the CLIP encoding of a caption", so the snippet works without any model loaded. ```python import lancedb db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data") tbl = db.open_table("val") seed = ( tbl.search() .select(["text_emb", "caption"]) .limit(1) .offset(42) .to_list()[0] ) hits = ( tbl.search(seed["text_emb"], vector_column_name="image_emb") .metric("cosine") .select(["image_id", "caption"]) .limit(10) .to_list() ) print("query caption:", seed["caption"]) for r in hits: print(f" {r['image_id']:>12} {r['caption'][:70]}") ``` Because OpenAI-style CLIP embeddings are normalized, cosine is the right metric and the first hit will typically be the source image itself — a useful sanity check. Swap `vector_column_name="image_emb"` for `text_emb` to do text→text retrieval against the canonical captions instead. Because the dataset also ships an `INVERTED` index on `caption`, the same query can be issued as a hybrid search that combines the dense vector with a keyword query. LanceDB merges the two result lists and reranks them in a single call, which is useful when a phrase like "yellow taxi" must literally appear in the caption but you still want CLIP to do the heavy lifting on visual similarity. ```python hybrid_hits = ( tbl.search(query_type="hybrid", vector_column_name="image_emb") .vector(seed["text_emb"]) .text("a man riding a surfboard") .select(["image_id", "caption"]) .limit(10) .to_list() ) for r in hybrid_hits: print(f" {r['image_id']:>12} {r['caption'][:70]}") ``` Tune `metric`, `nprobes`, and `refine_factor` on the vector side to trade recall against latency. ## Curate A typical curation pass for a captioning or contrastive-training workflow combines a content filter on the captions with a structural filter on the image. Stacking both inside a single filtered scan keeps the result small and explicit, and the bounded `.limit(500)` makes it cheap to inspect before committing the subset to anything downstream. ```python import lancedb db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data") tbl = db.open_table("val") candidates = ( tbl.search("surfer OR surfboard OR wave") .where("array_length(captions) >= 5", prefilter=True) .select(["image_id", "caption", "captions"]) .limit(500) .to_list() ) print(f"{len(candidates)} candidates; first caption: {candidates[0]['caption'][:80]}") ``` The result is a plain list of dictionaries, ready to inspect, persist as a manifest of `image_id`s, or feed into the Evolve and Train workflows below. The `image` column is never read, so the network traffic for a 500-row candidate scan is dominated by caption text rather than JPEG bytes. ## 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 `num_captions` and a `long_caption` flag, 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 split first. ```python import lancedb db = lancedb.connect("./coco-captions-2017-lance/data") # local copy required for writes tbl = db.open_table("val") tbl.add_columns({ "num_captions": "array_length(captions)", "long_caption": "length(caption) >= 80", }) ``` If the values you want to attach already live in another table (offline labels, classifier predictions, a second-pass caption from a different model), merge them in by joining on `image_id`: ```python import pyarrow as pa labels = pa.table({ "image_id": pa.array(["179765", "000139"]), "scene_label": pa.array(["beach", "kitchen"]), }) tbl.merge(labels, on="image_id") ``` 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., running a second CLIP variant 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 CLIP-style contrastive run, project the JPEG bytes and a sampled caption; for a reranker or probe on top of frozen features, project the precomputed embeddings instead. ```python import lancedb from lancedb.permutation import Permutation from torch.utils.data import DataLoader db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data") tbl = db.open_table("val") train_ds = Permutation.identity(tbl).select_columns(["image", "caption"]) loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4) for batch in loader: # batch carries only the projected columns; decode the JPEG bytes, # tokenize the captions, encode, contrastive loss... ... ``` Switching feature sets is a configuration change: passing `["image_emb", "text_emb"]` to `select_columns(...)` on the next run skips JPEG decoding entirely and reads only the cached 512-d vectors, which is the right shape for training a lightweight reranker or a linear probe. ## 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 db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data") tbl = db.open_table("val") print("Current version:", tbl.version) print("History:", tbl.list_versions()) print("Tags:", tbl.tags.list()) ``` Once you have a local copy, tag a version for reproducibility: ```python local_db = lancedb.connect("./coco-captions-2017-lance/data") local_tbl = local_db.open_table("val") local_tbl.tags.create("clip-vitb32-v1", local_tbl.version) ``` 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("val", version="clip-vitb32-v1") tbl_v5 = db.open_table("val", version=5) ``` Pinning supports two workflows. A retrieval system locked to `clip-vitb32-v1` keeps returning stable results while the dataset evolves in parallel — newly added embeddings or labels 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 and captions, so changes in metrics 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/coco-captions-2017-lance/data") remote_tbl = remote_db.open_table("test") batches = ( remote_tbl.search("surfer OR surfboard OR wave") .where("array_length(captions) >= 5") .select(["image_id", "image", "caption", "captions", "image_emb", "text_emb"]) .to_batches() ) local_db = lancedb.connect("./coco-surf-subset") local_db.create_table("train", batches) ``` The resulting `./coco-surf-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/coco-captions-2017-lance/data` for `./coco-surf-subset`. ## Source & license Converted from [`lmms-lab/COCO-Caption2017`](https://huggingface.co/datasets/lmms-lab/COCO-Caption2017). Original COCO 2017 annotations are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); the underlying images are subject to Flickr terms of service. Please review the [COCO Terms of Use](https://cocodataset.org/#termsofuse) before redistribution. ## Citation ``` @inproceedings{lin2014microsoft, title={Microsoft COCO: Common objects in context}, author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, booktitle={European Conference on Computer Vision (ECCV)}, year={2014} } ```