The timm/vit_so400m_patch16_siglip_256.v2_webli is a vision transformer model hosted on Hugging Face. It belongs to the SigLIP family and was pretrained on the WebLI dataset. The model addresses the need for image feature extraction in computer vision applications.
It is provided by the PyTorch Image Models library, also known as timm. The model card lists support for image-feature-extraction pipelines and direct loading through the Transformers library. Two arXiv papers are referenced in connection with its development: 2502.14786 and 2303.15343.
Usage examples show integration with timm via a create_model call that loads the pretrained weights from the Hugging Face hub. The same model can be instantiated with AutoModel or wrapped in a pipeline for feature extraction tasks. It is distributed with Safetensors weights and carries an Apache-2.0 license.
The entry appears under the timm organization on Hugging Face and includes tags for PyTorch, Safetensors, Transformers, webli, siglip, and siglip2. Notebooks and inference providers are mentioned as additional access methods.
Vit So400m Patch16 Siglip 256.v2 Webli sits in PulseGate's Other AI category. It focuses on extracting high-quality embeddings and features from images using a large-scale pretrained vision model. Vit So400m Patch16 Siglip 256.v2 Webli is an open-source project aimed at developers. The project is open source (Open Source). It runs on the web, API, and the command line.
It is developed by PyTorch Image Models, and the product first shipped in 2024. Among its 3 catalogued features are Image Feature Extraction, Vision Transformer, and SigLIP Architecture.
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