LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning

Augmented-Waste-Classifier-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224

Facial-Emotion-Detection-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224

Age-Classification-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 predict the age group of a person from an image using the SiglipForImageClassification architecture.

classify handwritten digits (0-9)

Fashion-Mnist-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 into Fashion-MNIST categories using the SiglipForImageClassification architecture.

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.

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.

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.

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

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

Human-vs-NonHuman-Detection 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 human or non-human using the SiglipForImageClassification architecture.

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

Painting-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 paintings into 126 domain categories using 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.

Gym-Workout-Classifier-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224

Alphabet-Sign-Language-Detection 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 sign language alphabet categories using the SiglipForImageClassification architecture.

SAT-Landforms-Classifier 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 satellite images into different landform categories using 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.