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

deepfake-detector-model-v1 is a vision-language encoder model fine-tuned from siglip2-base-patch16-512 for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the SiglipForImageClassification architecture.

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.

Watermark-Detection-SigLIP2 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for binary image classification. It is trained to detect whether an image contains a watermark or not, using the SiglipForImageClassification architecture.

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

siglip2-mini-explicit-content is an image classification vision-language encoder model fine-tuned from siglip2-base-patch16-512 for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the SiglipForImageClassification architecture.

Human-Action-Recognition is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for multi-class human action recognition. It uses the SiglipForImageClassification architecture to predict human activities from still images.

SigLIP2 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224

Anime-Classification-v1.0 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 anime-related images using the SiglipForImageClassification architecture.

nsfw-image-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-256 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering.

x-bot-profile-detection is a SigLIP2-based classification model designed to detect profile authenticity types on social media platforms (such as X/Twitter). It categorizes a profile image into four classes: bot, cyborg, real, or verified. Built on google/siglip2-base-patch16-224.

classify handwritten digits (0-9)

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.

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.

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.

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.

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.

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.