[ICCVW 2025] LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning
Command-line tool for extracting DINO, CLIP, SigLIP2, TIPSv2, RADIO, features for images and videos
Local-first semantic video search: text & image queries, subtitle OCR, preview/export, optional LAN sharing.
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.
Local image search engine powered by AI
Image infrastructure for storing, transforming, and delivering images through one API, with stable application paths, reusable variants, and flexible storage and delivery.
This is an extended version of the paper "TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection," accepted at ICASSP 2026.
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.
Facial-Emotion-Detection-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224
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.
Contrastive Olfaction-Language-Image Pre-training Model. The first-ever series of embeddings models for olfaction-vision-language applications in robotics and embodied AI - an extension of CLIP with olfaction.
This repository offers tools and guidance for fine-tuning the Siglip2 Vision Transformer (ViT) model. It includes scripts and best practices to adapt the model for custom datasets and tasks. Designed for researchers and developers, it ensures efficient fine-tuning and optimal performance for vision-based applicatio
Find any moment in your videos using Voice & Visuals. A full-stack AI search engine.
Identify mountain peaks in your photos using AI—zero-shot retrieval, landmark re-ranking, and geospatial priors.
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.
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.
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.
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.
Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer (ICCV 2025).
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