Score image-text similarity using CLIP or SigLIP models
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CLIP Score is a tool designed for image captioning that allows users to score the similarity between an image and a text description. It leverages advanced AI models such as CLIP (Contrastive Language–Image Pretraining) or SigLIP to evaluate how well an image matches a given caption. This scoring system is useful for applications like image retrieval, caption generation, and quality assessment of image-text pairs.
• Image-Text Similarity Scoring: Measures how closely an image matches a text description using state-of-the-art models.
• Support for Multiple Models: Works with both CLIP and SigLIP models, offering flexibility in scoring approaches.
• Fast and Efficient: Designed for quick computations, making it suitable for large-scale applications.
• Customizable: Users can fine-tune settings to adapt to specific use cases.
• Integration-Friendly: Can be easily integrated into existing workflows for image-based tasks.
What models does CLIP Score support?
CLIP Score supports both CLIP (Contrastive Language–Image Pretraining) and SigLIP models, allowing users to choose the best model for their specific needs.
How does the scoring work?
The scoring is based on the similarity between the image and text embeddings generated by the selected model. A higher score indicates a stronger match between the image and the text.
Can I use CLIP Score for real-time applications?
Yes, CLIP Score is designed to be fast and efficient, making it suitable for real-time applications such as image retrieval or caption validation.