Add BioMedFlickr parquet retrieval benchmark

README.md

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+---
+license: other
+task_categories:
+- image-to-text
+- visual-question-answering
+language:
+- en
+pretty_name: BioMedFlickr
+size_categories:
+- 1K<n<10K
+tags:
+- medical
+- pathology
+- retrieval
+- clip
+- biomedical
+- flickr
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: caption
+    dtype: string
+  - name: caption_raw
+    dtype: string
+  - name: title
+    dtype: string
+  - name: key
+    dtype: string
+  - name: date_uploaded
+    dtype: string
+  - name: flickr_id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: nsid
+    dtype: string
+  - name: category
+    dtype: string
+  - name: tags
+    sequence: string
+  - name: width
+    dtype: int32
+  - name: height
+    dtype: int32
+  splits:
+  - name: test
+    num_examples: 7185
+  download_size: 906819144
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+---
+
+# 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
+
+```python
+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)
+
+```python
+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](https://github.com/) package and a
+CLIP-style `model_dict` (`model`, `tokenizer`, `preprocess`, `device`):
+
+```python
+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.

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