Coral-Health is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify coral reef images into two health conditions using the SiglipForImageClassification architecture.

Multilabel-Portrait-SigLIP2 is a vision-language model fine-tuned from google/siglip2-base-patch16-224 using the SiglipForImageClassification architecture. It classifies portrait-style images into one of the following visual portrait categories:

Multilabel-GeoSceneNet is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for multi-label image classification. It is designed to recognize and label multiple geographic or environmental elements in a single image using the SiglipForImageClassification architecture.

Fashion-Product-Usage is a vision-language model fine-tuned from google/siglip2-base-patch16-224 using the SiglipForImageClassification architecture. It classifies fashion product images based on their intended usage context.

shoe-type-detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for multi-class image classification. It is trained to detect different types of shoes such as Ballet Flats, Boat Shoes, Brogues, Clogs, and Sneakers. The model uses the SiglipForImageClassification architecture.

Implementation of PaliGemma

Flood-Image-Detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for binary image classification. It is trained to detect whether an image contains a flooded scene or non-flooded environment. The model uses the SiglipForImageClassification architecture.

PussyCat-vs-Doggie-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images as either a cat or a dog using the SiglipForImageClassification architecture.

Leverage SigLIP 2's capabilities using LitServe.