README.md

---
license: other
task_categories:
- image-to-text
- image-text-to-text
- image-feature-extraction
language:
- en
tags:
- flickr30k
- image-captioning
- vision-language
- lance
- clip-embeddings
pretty_name: flickr30k-lance
size_categories:
- 10K<n<100K
---
# Flickr30k (Lance Format)

Lance-formatted version of [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/) (re-distributed via [`lmms-lab/flickr30k`](https://huggingface.co/datasets/lmms-lab/flickr30k)) — **31,783 images, each paired with 5 human-written captions**, with CLIP image **and** text embeddings stored inline and pre-built ANN indices on both.

## Key features

- **Inline images** — full JPEG bytes per row.
- **Pre-computed CLIP embeddings** for both image and caption text — `IVF_PQ` indices on both columns let you do cross-modal retrieval (image→caption or caption→image) without any model at query time.
- **Full-text inverted index** on the canonical caption.
- Self-contained: no sidecar files or external image downloads.

## Schema

| Column | Type | Notes |
|---|---|---|
| `id` | `int64` | Row index |
| `image` | `large_binary` | Inline JPEG bytes |
| `image_id` | `string` | Original Flickr image id |
| `filename` | `string` | Original filename (e.g. `1000092795.jpg`) |
| `captions` | `list<string>` | All 5 captions for the image |
| `caption` | `string` | First caption — used as canonical text for FTS / quick browsing |
| `image_emb` | `fixed_size_list<float32, 512>` | CLIP image embedding (cosine-normalized) |
| `text_emb` | `fixed_size_list<float32, 512>` | CLIP text embedding of the canonical caption |

## Pre-built indices

- `IVF_PQ` on `image_emb` — `metric=cosine`
- `IVF_PQ` on `text_emb` — `metric=cosine` (cross-modal retrieval works out of the box)
- `INVERTED` on `caption`
- `BTREE` on `image_id`

## Splits

A single `train.lance` table containing all 31,783 rows (the `lmms-lab/flickr30k` redistribution exposes them as a single split). The original train/val/test labels are not preserved in the source parquet.

## Load with Lance

```python
import lance

ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())
```

## 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.

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
tbl = db.open_table("train")
print(f"LanceDB table opened with {len(tbl)} image-caption pairs")
```

## Cross-modal text→image search

```python
import lance
import pyarrow as pa
import open_clip
import torch

# 1. Encode the query text once with the same CLIP model used at conversion.
model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
tokenizer = open_clip.get_tokenizer("ViT-B-32")
model = model.eval().cuda().half()
with torch.no_grad():
    q = model.encode_text(tokenizer(["a man surfing at sunset"]).cuda())
    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]

ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
emb_field = ds.schema.field("image_emb")
query = pa.array([q.tolist()], type=emb_field.type)

# 2. Nearest-neighbour search against the image embedding index.
hits = ds.scanner(
    nearest={"column": "image_emb", "q": query[0], "k": 10, "nprobes": 16, "refine_factor": 30},
    columns=["image_id", "caption"],
).to_table().to_pylist()
for h in hits:
    print(h)
```

### LanceDB cross-modal text→image search

```python
import lancedb, open_clip, torch

model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
tokenizer = open_clip.get_tokenizer("ViT-B-32")
model = model.eval().cuda().half()
with torch.no_grad():
    q = model.encode_text(tokenizer(["a man surfing at sunset"]).cuda())
    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]

db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search(q.tolist(), vector_column_name="image_emb")
    .metric("cosine")
    .select(["image_id", "caption"])
    .limit(10)
    .to_list()
)
```

## Image→caption (image-to-text retrieval)

```python
ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
ref = ds.take([0], columns=["image_emb", "caption"]).to_pylist()[0]
emb_field = ds.schema.field("text_emb")
query = pa.array([ref["image_emb"]], type=emb_field.type)
neighbors = ds.scanner(
    nearest={"column": "text_emb", "q": query[0], "k": 10},
    columns=["caption"],
).to_table().to_pylist()
```

### LanceDB image→caption search

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
tbl = db.open_table("train")

ref = tbl.search().limit(1).select(["image_emb", "caption"]).to_list()[0]
query_embedding = ref["image_emb"]

results = (
    tbl.search(query_embedding, vector_column_name="text_emb")
    .metric("cosine")
    .select(["caption"])
    .limit(10)
    .to_list()
)
```

## Full-text search on captions

```python
import lance
ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
hits = ds.scanner(
    full_text_query="dog playing in the snow",
    columns=["image_id", "caption"],
    limit=10,
).to_table().to_pylist()
```

### LanceDB full-text search

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/flickr30k-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search("dog playing in the snow")
    .select(["image_id", "caption"])
    .limit(10)
    .to_list()
)
```

## Working with images

```python
from pathlib import Path
import lance
ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
row = ds.take([0], columns=["image", "filename"]).to_pylist()[0]
Path(row["filename"]).write_bytes(row["image"])
```

## Why Lance?

- One dataset carries images + image embeddings + text embeddings + indices — no sidecar files.
- On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
- Schema evolution: add columns (new captions, alternate embeddings, moderation labels) without rewriting the data.

## Source & license

Converted from [`lmms-lab/flickr30k`](https://huggingface.co/datasets/lmms-lab/flickr30k), which is itself a parquet redistribution of the [original Flickr30k corpus](https://shannon.cs.illinois.edu/DenotationGraph/). Original images come from Flickr; review the Flickr30k licensing terms before redistribution.

## Citation

```
@article{young2014image,
  title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
  author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
  journal={Transactions of the Association for Computational Linguistics},
  volume={2},
  pages={67--78},
  year={2014}
}
```