Clipart-126-DomainNet 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 clipart images into 126 domain categories using the SiglipForImageClassification architecture

Hand-Gesture-2-Robot 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 recognize hand gestures and map them to specific robot commands using the SiglipForImageClassification architecture.

Deepfake vs Real is a dataset designed for image classification, distinguishing between deepfake and real images.

open-deepfake-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-512 for binary image classification. It is trained to detect whether an image is fake or real using the OpenDeepfake-Preview dataset. The model uses the SiglipForImageClassification architecture.

Food-101-93M is a fine-tuned image classification model built on top of google/siglip2-base-patch16-224 using the SiglipForImageClassification architecture. It is trained to classify food images into one of 101 popular dishes, derived from the Food-101 dataset.

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.

Face-Mask-Detection is a binary image classification model based on google/siglip2-base-patch16-224, trained to detect whether a person is wearing a face mask or not. This model can be used in public health monitoring, access control systems, and workplace compliance enforcement.

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:

Multisource-121-DomainNet 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 into 121 domain categories using the SiglipForImageClassification architecture.

facial-age-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 and classify human faces into age groups ranging from early childhood to elderly adults. The model uses the SiglipForImageClassification architecture.

Fire-Detection-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 detect fire, smoke, or normal conditions using the SiglipForImageClassification architecture.

Hand-Gesture-2-Robot 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 recognize hand gestures and map them to specific robot commands using the SiglipForImageClassification architecture.

Clipart-126-DomainNet 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 clipart images into 126 domain categories using the SiglipForImageClassification architecture

open-age-detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for multi-class image classification. It is trained to classify the estimated age group of a person from an image. The model uses the SiglipForImageClassification architecture.

Document-Type-Detection is a multi-class image classification model based on google/siglip2-base-patch16-224, trained to detect and classify types of documents from scanned or photographed images. This model is helpful for automated document sorting, OCR pipelines, and digital archiving systems.

Gender-Classifier-Mini 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 based on gender using the SiglipForImageClassification architecture.

Bird-Species-Classifier-526 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224

Traffic-Density-Classification 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 into traffic density categories using the SiglipForImageClassification architecture.

IndoorOutdoorNet 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 Indoor or Outdoor using the SiglipForImageClassification architecture..

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