SigLIP
Improved vision-language model by Google using sigmoid loss for contrastive learning.
About
SigLIP by Google is a vision-language model that replaces the softmax contrastive loss of CLIP with a sigmoid loss computed on each image-text pair, which scales better and improves zero-shot performance. It produces strong image and text embeddings for zero-shot classification and retrieval and is available through Hugging Face. It is trained with the big_vision JAX codebase. Released under the Apache 2.0 license.
Reviews (0)
Leave a Review
No reviews yet. Be the first to review!
Details
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Intermediate (3/5)
- License
- Apache-2.0
- Minimum VRAM
- 4 GB
- Added
- Apr 3, 2026
Related Tools
Foundation model for monocular depth estimation by TikTok.
Open-vocabulary object detection model by Google using vision transformers.
OpenMMLab detection toolbox with 300+ pre-trained models and 80+ algorithms.
State-of-the-art real-time object detection supporting YOLOv5 through v11.
Robust multi-object tracking combining motion and appearance cues.
Additive angular margin loss for deep face recognition.