mirror of
https://github.com/lancedb/lancedb.git
synced 2025-12-26 14:49:57 +00:00
7d0127b376
### Summary This PR adds **SigLIP** (Sigmoid Loss Image Pretraining) as a new embedding model in the LanceDB embedding registry. SigLIP improves image-text alignment performance using sigmoid-based contrastive loss and offers robust zero-shot generalization. Fixes #2498 ### What’s Implemented #### 1. `SigLIP` Embedding Class * Added `SigLIP` support under `python/lancedb/embeddings/siglip.py` * Implements: * `compute_source_embeddings` * `_batch_generate_embeddings` * Normalization logic * Batch-wise progress logging for image embedding #### 2. Registry Integration * Registered `SigLIP` in `embeddings/__init__.py` * `SigLIP` now usable via `connect(..., embedding="siglip")` #### 3. Evaluation Benchmark Support * Added SigLIP to `test_embeddings_slow.py` for side-by-side benchmarking with OpenCLIP and ImageBind ### New Test Methods #### `test_siglip` * End-to-end test to verify embeddings table creation and vector shape for SigLIP  #### `test_siglip_vs_openclip_vs_imagebind_benchmark_full` * Benchmarks: * **Recall\@1 / 5 / 10** * **mAP (Mean Average Precision)** * **Embedding & Search Latency** * Dimensionality reporting  ### Notes * SigLIP outputs 768D embeddings (vs 512D for OpenCLIP) * Benchmark shows competitive performance despite higher dimensionality * I'm still new to contributing to open-source and learning as I go. Please feel free to suggest any improvements — I'm happy to make changes!
LanceDB
A Python library for LanceDB.
Installation
pip install lancedb
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.