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README.md

@@ -4,6 +4,7 @@ task_categories:
   - feature-extraction
   - image-classification
   - zero-shot-classification
+  - image-to-image
 language:
   - en
 tags:
@@ -12,85 +13,171 @@ tags:
   - laion
   - image-embeddings
   - natural-images
+  - image-retrieval
 size_categories:
   - 100M<n<1B
 ---
 
 # ReLAION-2B Natural Embeddings
 
-CLIP ViT-H/14 embeddings for ~500M natural/photographic images from the ReLAION-2B dataset.
+Pre-computed **CLIP ViT-H/14 embeddings** for ~500 million natural photographs from ReLAION-2B.
 
-## Overview
+## Quick Start
+
+```python
+from datasets import load_dataset
+import numpy as np
 
-This dataset contains pre-computed CLIP embeddings for images classified as "natural" (score > 0.7) from the [relaion2b-natural](https://huggingface.co/datasets/andropar/relaion2b-natural) dataset.
+# Load with streaming (recommended for this size)
+ds = load_dataset("andropar/relaion2b-natural-embeddings", streaming=True)
+
+for row in ds['train']:
+    url = row['url']
+    embedding = np.array(row['embedding'])  # 768-dim CLIP embedding
+    score = row['natural_score']  # 0.7 - 1.0
+    break
+```
+
+## Overview
 
 | | |
 |---|---|
-| **Rows** | ~514 million |
+| **Total embeddings** | ~514 million |
 | **Embedding model** | [CLIP ViT-H/14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) |
 | **Embedding dimensions** | 768 |
-| **Source** | [ReLAION-2B-en-research-safe](https://huggingface.co/datasets/laion/relaion2B-en-research-safe) |
-| **Natural score threshold** | > 0.7 |
+| **Natural score range** | 0.7 - 1.0 (filtered for natural photographs) |
 | **Format** | Parquet (Snappy compressed) |
+| **Total size** | ~711 GB |
+
+## Example Images
+
+All images in this dataset have natural scores > 0.7. Examples from different score ranges:
+
+**Natural score 0.7 - 0.8:**
+![Examples with scores 0.7-0.8](examples/natural_0.7-0.8.png)
+
+**Natural score 0.8 - 0.9:**
+![Examples with scores 0.8-0.9](examples/natural_0.8-0.9.png)
+
+**Natural score 0.9 - 1.0:**
+![Examples with scores 0.9-1.0](examples/natural_0.9-1.0.png)
+
+*Thumbnails shown solely to illustrate dataset characteristics. Source: ReLAION-2B-en-research-safe (Apache 2.0). Underlying images remain under the copyright of their original creators.*
 
 ## Dataset Structure
 
 | Column | Type | Description |
 |--------|------|-------------|
 | `url` | string | Image URL from ReLAION-2B |
-| `natural_score` | float32 | Naturalness prediction (0.7-1.0 range) |
+| `natural_score` | float32 | Naturalness prediction (0.7 - 1.0) |
 | `feature_row_id` | int64 | Row index in original LAION-2B embeddings |
-| `embedding` | float32[768] | CLIP ViT-H/14 embedding |
+| `embedding` | float32[768] | CLIP ViT-H/14 image embedding |
 
-## Usage
+Files are named `relaion2b_features_*.parquet` (2,298 files total).
 
-**Load with PyArrow:**
+## Usage Examples
+
+**Load embeddings with PyArrow:**
 ```python
 import pyarrow.parquet as pq
+import numpy as np
 
 # Load a single file
-table = pq.read_table("relaion2b_features_00000.parquet")
+table = pq.read_table("data/relaion2b_features_00000.parquet")
 
-# Access embeddings
-import numpy as np
+# Convert to numpy
 embeddings = np.array([e.as_py() for e in table['embedding']])
 urls = table['url'].to_pylist()
+scores = table['natural_score'].to_pylist()
+
+print(f"Loaded {len(embeddings)} embeddings, shape: {embeddings.shape}")
 ```
 
-**Load with Hugging Face Datasets:**
+**Image similarity search:**
 ```python
-from datasets import load_dataset
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Assuming you have a query embedding from CLIP
+query_embedding = ...  # Your 768-dim CLIP embedding
+
+# Find most similar images
+similarities = cosine_similarity([query_embedding], embeddings)[0]
+top_k = np.argsort(similarities)[-10:][::-1]
+
+for idx in top_k:
+    print(f"Score: {similarities[idx]:.3f}, URL: {urls[idx]}")
+```
+
+**Build a FAISS index for fast search:**
+```python
+import faiss
+import numpy as np
 
-dataset = load_dataset("andropar/relaion2b-natural-embeddings")
+# Load embeddings from multiple files
+all_embeddings = []
+for i in range(10):  # First 10 files
+    table = pq.read_table(f"data/relaion2b_features_{i:05d}.parquet")
+    embs = np.array([e.as_py() for e in table['embedding']], dtype=np.float32)
+    all_embeddings.append(embs)
+
+embeddings = np.vstack(all_embeddings)
+
+# Build index
+index = faiss.IndexFlatIP(768)  # Inner product (for normalized vectors)
+faiss.normalize_L2(embeddings)
+index.add(embeddings)
+
+# Search
+query = np.random.randn(1, 768).astype(np.float32)
+faiss.normalize_L2(query)
+distances, indices = index.search(query, k=10)
 ```
 
-**Streaming (recommended for large-scale use):**
+**Stream with HuggingFace datasets:**
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("andropar/relaion2b-natural-embeddings", streaming=True)
-for example in dataset['train']:
-    embedding = example['embedding']
-    url = example['url']
-    # process...
+ds = load_dataset("andropar/relaion2b-natural-embeddings", streaming=True)
+
+# Process in batches
+batch = []
+for i, row in enumerate(ds['train']):
+    batch.append(row['embedding'])
+    if len(batch) >= 1000:
+        # Process batch
+        embeddings = np.array(batch)
+        # ... your processing ...
+        batch = []
+    if i >= 10000:
+        break
 ```
 
 ## Use Cases
 
-- Image similarity search and retrieval
-- Zero-shot image classification
-- Training image-text models
-- Clustering and analysis of natural images
-- Building image search indices
+- **Image similarity search:** Find visually similar images in a large corpus
+- **Zero-shot classification:** Use CLIP embeddings for classification without training
+- **Clustering:** Discover visual themes and patterns in natural photographs
+- **Training data:** Use as features for downstream models
+- **Research:** Study visual representations at scale
 
 ## Related Datasets
 
-- [andropar/relaion2b-natural](https://huggingface.co/datasets/andropar/relaion2b-natural) - Natural scores for all 2.1B ReLAION-2B images
+- **[andropar/relaion2b-natural](https://huggingface.co/datasets/andropar/relaion2b-natural)** - Natural scores for all 2.1B ReLAION-2B images (use this to get URLs for images with any score threshold)
 
 ## Attribution
 
 - **Source dataset:** [ReLAION-2B-en-research-safe](https://huggingface.co/datasets/laion/relaion2B-en-research-safe) by LAION
-- **Embedding model:** [CLIP ViT-H/14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) by LAION
+- **Embedding model:** [CLIP ViT-H/14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) trained on LAION-2B
+
+## Licensing / Content
+
+This repository contains only **metadata and derived features** (URLs, natural scores, embeddings). No images are distributed.
+
+- The underlying images are hosted by third-party websites and remain under their original copyrights and terms of use.
+- Our additions (embeddings, documentation) are released under **CC-BY 4.0**.
+- This dataset is based on [ReLAION-2B-en-research-safe](https://huggingface.co/datasets/laion/relaion2B-en-research-safe), which is licensed under **Apache 2.0**.
+- Please check license compatibility for any commercial usage.
 
 ## Citation
 
@@ -107,7 +194,11 @@ for example in dataset['train']:
 
 ## Limitations
 
-- Embeddings are from CLIP ViT-H/14, which was trained on web data and may have biases
+- Embeddings are from CLIP ViT-H/14, which was trained on web data and may reflect its biases
 - "Naturalness" is based on a learned classifier - see [relaion2b-natural](https://huggingface.co/datasets/andropar/relaion2b-natural) for methodology
-- Some URLs may be broken or point to different/removed images
+- Some URLs may be broken or point to different/removed images over time
 - Intended for research use
+
+---
+
+Questions or issues? Open a discussion!