[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 image search engine powered by AI
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.
A service for managing your application's images. Designed to fit your existing backend. Validate, store, transform, and manage images throughout their lifecycle.
[ICASSP 2026] TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
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.
Smart desktop tool for image similarity & natural language text search. Powered by Google's SigLIP 2, LanceDB vector database, OpenCLIP, and PyQt.
A fully-offline, privacy-first, multi-modal desktop search engine for local images and videos. Search by natural language, OCR text, or visual similarity — 100% private, zero cloud, free and open source.
Facial-Emotion-Detection-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224
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.
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.
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.
Official Repo for Revisiting Vision–Language Foundations for No-Reference Image Quality Assessment
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