Popular GitHub Repositories for Siglip

Custom ComfyUI nodes for Vision Language Models, Large Language Models, Image to Music, Text to Music, Consistent and Random Creative Prompt Generation

Inference and fine-tuning examples for vision models from ? Transformers

Official repository of "TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models".

State-of-the-art CLIP/SigLIP embedding models finetuned for the fashion domain. +57% increase in evaluation metrics vs FashionCLIP 2.0.

[ICLR 2025] - Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Official PyTorch implementation of the WACV 2025 Oral paper "Composed Image Retrieval for Training-FREE DOMain Conversion".

Low-latency ONNX and TensorRT based zero-shot classification and detection with contrastive language-image pre-training based prompts

State-of-the-art embedding models fine-tuned for the ecommerce domain. +67% increase in evaluation metrics vs ViT-B-16-SigLIP.

A minimal, but effective implementation of CLIP (Contrastive Language-Image Pretraining) in PyTorch

Local image search engine powered by CLIP/SigLIP

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.

Code for Post-hoc Probabilistic Vision-Language Models

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

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.

Notes for the Vision Language Model implementation by Umar Jamil

classify handwritten digits (0-9)

A custom Vision-Language Model (VLM) built from scratch, using SigLip for contrastive learning and a ViT-based encoder to generate meaningful image captions and semantic descriptions.

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.

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.

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.