Update README.md

README.md

@@ -1,33 +1,113 @@
 ---
 license: mit
+task_categories:
+- zero-shot-image-classification
+- image-classification
+- image-to-text
+- text-to-image
 language:
 - en
-pretty_name: EntityNet
 size_categories:
 - 10M<n<100M
-task_categories:
-- image-to-text
-- text-to-image
-- image-classification
-- visual-question-answering
+tags:
+- clip
+- vision-language
+- knowledge-graph
+- wikidata
+- image-text
+- biodiversity
+- species-recognition
+- fine-grained-classification
+pretty_name: EntityNet
 ---
 
-# EntityNet: Using Knowledge Graphs to harvest datasets for efficient CLIP model training
+# EntityNet
+
+**33M web images paired with 46M texts, harvested by querying image search with 135k visual entities from Wikidata.**
+
+EntityNet is the training set from *Using Knowledge Graphs to Harvest Datasets for Efficient CLIP Model Training* (GCPR 2025). Instead of scraping the web and filtering afterwards, we start from a knowledge graph: we extract visual entities and their attributes from Wikidata, turn them into search queries, and collect the returned images together with their alt texts.
+
+[Paper](https://arxiv.org/abs/2505.02746) · [Code](https://github.com/lmb-freiburg/entitynet) · [Project page](https://entity-net.github.io) · [Models](https://huggingface.co/collections/lmb-freiburg/entitynet-6810b98ea9288fef9b6c09ca)
+
+## Why use it
+
+- **Compute-efficient CLIP training.** CLIP models trained from scratch on EntityNet reach strong ImageNet and fine-grained results with a fraction of the compute of models trained on billion-scale web crawls.
+- **Structured labels, not just alt text.** Every image traces back to the Wikidata entity and query that found it. Mixing these entity labels with the alt texts during training works better than either alone.
+- **Domain experts from a subset.** A 10M-image subset of living organisms (animals, plants, fungi) trains a species-recognition model that outperforms BioCLIP (trained on the same order of data, TreeOfLife-10M) on the Rare Species benchmark.
+
+## What's covered
+
+135k entities from 21 Wikidata super-categories:
+
+product, substance, physical tool, animal, plant, material, vehicle, geographical feature, food, architectural structure, anatomical structure, facility, physical activity, clothing, building, musical instrument, organ, furniture, body of water, weather, precipitation
+
+The full entity list with Wikidata IDs, attributes and parent relations is in [`entitynet-entities.parquet`](https://github.com/lmb-freiburg/entitynet/blob/main/entitynet-entities.parquet), and the generated search queries are in [`entitynet-queries.parquet`](https://github.com/lmb-freiburg/entitynet/blob/main/entitynet-queries.parquet).
+
+## Data format
+
+This repository contains **URLs and texts, not image files.** Use the download scripts in the GitHub repo to fetch the images and build webdataset shards.
+
+| Split | Rows |
+|---|---|
+| train | 31.7M |
+| validation | 19.4k |
+| test | 19.3k |
+
+Each row is one unique (deduplicated) image:
+
+| Column | Description |
+|---|---|
+| `key` | Unique sample ID |
+| `qh` | Hashes of the search queries that returned this image (join with `entitynet-queries.parquet`) |
+| `texts` | Alt texts and page titles collected for the image |
+| `images_link` | Image URLs (several URLs can point to the same image) |
+| `images_contextlink` | URLs of the pages the image appeared on |
+| `images_width`, `images_height` | Image size per URL |
+
+## Quickstart
+
+Browse the metadata:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("lmb-freiburg/entitynet", split="validation")
+print(ds[0]["texts"], ds[0]["images_link"])
+```
+
+Download images and build training shards (see the [GitHub README](https://github.com/lmb-freiburg/entitynet) for setup):
+
+```bash
+python -m entitynet.cli.urlbuild_step1_metadata --splits val,test,train
+python -m entitynet.cli.urlbuild_step2_images
+python -m entitynet.cli.urlbuild_step3_tars
+```
+
+Or skip training and use a pretrained model with open_clip:
+
+```python
+import open_clip
+model, _, preprocess = open_clip.create_model_and_transforms("hf-hub:lmb-freiburg/CLIP-ViT-B-16-EntityNet-33M")
+```
+
+## Limitations
 
-Dataloader and instructions can be found here: [https://github.com/lmb-freiburg/entitynet](https://github.com/lmb-freiburg/entitynet)
+- Images come from the open web. URLs go stale over time, so a fresh download will recover fewer than 33M images.
+- Image copyright stays with the original owners. The MIT license covers our metadata and code, not the images.
+- Web images carry web biases: stock photos and product shots are over-represented, and artistic or sketch-style depictions are under-represented.
 
-If you use the dataset, code, or results, please cite:
+## Citation
 
 ```bibtex
-@inproceedings{ging2025entitynet,
+@inproceedings{ging2026entitynet,
   author    = {Simon Ging and Sebastian Walter and Jelena Bratuli{\'c} and Johannes Dienert and Hannah Bast and Thomas Brox},
   title     = {Using Knowledge Graphs to Harvest Datasets for Efficient {CLIP} Model Training},
   booktitle = {Pattern Recognition, 47th {DAGM} German Conference, {DAGM} {GCPR} 2025, Freiburg, Germany, September 23--26, 2025, Proceedings},
   series    = {Lecture Notes in Computer Science},
   publisher = {Springer Nature Switzerland},
-  year      = {2025},
+  year      = {2026},
   pages     = {287--302},
   isbn      = {978-3-032-12840-9},
   doi       = {10.1007/978-3-032-12840-9_19}
 }
-```
+```