siglip-so400m-patch14-384

Model · Zero shot image classification

siglip2-so400m-patch14-384

Google

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero-shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up…

Model · Zero shot image classification

clip-vit-large-patch14

OpenAI

Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. January 2021 The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked…

Model · Zero shot image classification

CLIP-ViT-L-14-laion2B-s32B-b82K

LAION eV

A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/openclip). Model training ('babysitting') done by Ross Wightman on the JUWELS Booster supercomputer. See acknowledgements below. As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts…

Model · Zero shot image classification

siglip2-base-patch16-256

Google

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero-shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up…

Model · Zero shot image classification

siglip2-base-patch16-naflex

Google

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero-shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip2 documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up…

Model · Zero shot image classification

siglip2-base-patch16-224

Google

SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero-shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. SigLIP 2 adds some clever training objectives on top of SigLIP: SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). The model was trained on up…