BioMedFlickr

BioMedFlickr is a biomedical image–caption retrieval benchmark built from public Flickr pathology / microscopy albums. Each example is a single image paired with a cleaned English caption. The Hugging Face split matches the evaluation set used in the EVVLM retrieval notebook (dev_retrival-Copy1.ipynb): captions shorter than 10 characters after cleaning are dropped, leaving 7185 test pairs.

Images are stored as original JPEGs inside parquet files so the dataset viewer is enabled (~865 MB download).

Load

from datasets import load_dataset

ds = load_dataset("Alejandro98/BioMedFlickr", split="test")
print(ds)
print(ds[0]["caption"])
ds[0]["image"]

Columns:

column	description
image	JPEG image (datasets.Image)
caption	cleaned caption used for retrieval
caption_raw	original Flickr text before cleaning
title	Flickr title
key	shard sample id
date_uploaded	Flickr upload timestamp
flickr_id	Flickr photo id (when available)
url	Flickr image URL (when available)
nsid	Flickr owner nsid
category	source album / caption file
tags	Flickr tags
width, height	original pixel size

Retrieval protocol

This is a 1-to-1 paired retrieval task. Encode every image and every cleaned caption, L2-normalize the embeddings, then use inner-product search (cosine). The relevant item for example i is the pair at the same index.

Reported metrics are Recall@k for k in {1, 10, 100, 1000}, in both directions:

• Image-to-text: query with image embeddings against the caption index
• Text-to-image: query with caption embeddings against the image index

Do not apply extra caption cleaning at eval time; caption is already the string used in the original notebook. Resize / normalize images with your model's own preprocess (the notebook used 224×224 only as a loader convenience; CLIP-style preprocessors already resize).

Minimal eval script (OpenCLIP + FAISS)

import numpy as np
import torch
import faiss
import open_clip
from datasets import load_dataset
from torch.utils.data import DataLoader

ds = load_dataset("Alejandro98/BioMedFlickr", split="test")

model_name, pretrained = "ViT-L-14", "openai"
model, _, preprocess = open_clip.create_model_and_transforms(
    model_name, pretrained=pretrained
)
tokenizer = open_clip.get_tokenizer(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device).eval()


def collate(batch):
    images = torch.stack([preprocess(ex["image"].convert("RGB")) for ex in batch])
    captions = [ex["caption"] for ex in batch]
    return {"image": images, "caption": captions}


loader = DataLoader(ds, batch_size=64, collate_fn=collate)

image_embeddings, text_embeddings = [], []
with torch.no_grad():
    for batch in loader:
        images = batch["image"].to(device)
        texts = tokenizer(batch["caption"]).to(device)
        ie = model.encode_image(images)
        te = model.encode_text(texts)
        ie = ie / ie.norm(dim=-1, keepdim=True)
        te = te / te.norm(dim=-1, keepdim=True)
        image_embeddings.append(ie.cpu().numpy())
        text_embeddings.append(te.cpu().numpy())

image_embeddings = np.concatenate(image_embeddings).astype("float32")
text_embeddings = np.concatenate(text_embeddings).astype("float32")


def recall_at_k(gallery, queries, ks=(1, 10, 100, 1000)):
    index = faiss.IndexFlatIP(gallery.shape[1])
    index.add(gallery)
    metrics = {}
    for k in ks:
        _, retrieved = index.search(queries, k)
        hits = np.array([i in row for i, row in enumerate(retrieved)])
        metrics[k] = float(hits.mean() * 100.0)
    return metrics


# image queries -> caption gallery  (image-to-text)
i2t = recall_at_k(text_embeddings, image_embeddings)
# caption queries -> image gallery  (text-to-image)
t2i = recall_at_k(image_embeddings, text_embeddings)

print("image-to-text R@k", i2t)
print("text-to-image R@k", t2i)

Recall@k is the fraction of queries whose paired index appears in the top-k neighbors. With ~7185 pairs, chance R@1 is about 0.014%.

The original notebook also reports a 95% t-interval around each recall. You can recover that from the per-query hit vector (hits above).

Using EVVLM

If you already have the evvlm package and a CLIP-style model_dict (model, tokenizer, preprocess, device):

from datasets import load_dataset
from torch.utils.data import DataLoader
from evvlm.inference.embedding.utils import process_image, get_features

ds = load_dataset("Alejandro98/BioMedFlickr", split="test")

def collate(batch):
    return {
        "image": [ex["image"].convert("RGB") for ex in batch],
        "caption": [ex["caption"] for ex in batch],
    }

dataloader = DataLoader(ds, batch_size=64, collate_fn=collate)
# then reuse generate_embeddings / get_top_k_metrics from the notebook

Construction

1. Public biomedical Flickr albums were serialized as webdataset shards (jpg, txt, title, dateuploaded).
2. Captions are cleaned with the notebook clean_caption rules (strip contribution / credit / HTML tails, collapse whitespace).
3. Pairs whose cleaned caption is shorter than 10 characters are removed. That is the only example filter; it yields 7185 pairs.
4. Original JPEG bytes are written to parquet (no 224 resize) together with Flickr metadata when a sidecar JSON exists.

Caption length on this split (from the notebook): median 78 characters (min 10, max 3287). CLIP token counts with ViT-L-14: median 23 (min 4, max 77).

Reference results

Numbers below come from dev_retrival-Copy1.ipynb on this same filtered set. The notebook labeled “image to text” as text queries against the image index (standard text-to-image) and “text to image” as image queries against the text index (standard image-to-text). We keep the notebook column names so CSV dumps stay comparable.

Recall is percent (higher is better).

model	I2T* R@1	I2T R@10	I2T R@100	I2T R@1000	T2I* R@1	T2I R@10	T2I R@100	T2I R@1000
PMC-CLIP	0.026	0.142	1.382	13.017	0.000	0.181	1.175	12.965
BioMedCLIP	3.435	11.816	34.427	72.805	4.106	13.494	34.633	69.977
ViT-H-14-378-quickgelu / dfn5b	3.900	13.753	35.008	71.113	4.081	12.745	32.619	65.586
ViT-L-14 / DataComp XL CLIP	2.596	9.749	26.085	58.148	2.763	9.155	24.432	53.654
CPT ViT-L-14 (biomed continued pretrain)	4.148	15.143	38.344	76.785	4.134	13.751	36.089	72.053

*Notebook names: I2T = image to text (text → image gallery), T2I = text to image (image → text gallery).

License and source

Images and captions were collected from public Flickr albums (pathology, microscopy, and related biomedical photography). Flickr items keep their original photographer licenses; this repo does not re-license third-party photos. If you are a rights holder and want an image removed, open an issue on the dataset page.

Intended use is research evaluation of biomedical vision–language models, not clinical deployment.