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AI モデル 76

• ViT-B-32__openai↗

  immich-app

  This repo contains ONNX exports for the associated CLIP model by OpenCLIP. See the OpenCLIP repo for more info. This repo is specifically intended for use with Immich, a self-hosted photo library.

• CLIP-ViT-bigG-14-laion2B-39B-b160k↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Mitchell Wortsman on the stability.ai cluster.

• CLIP-ViT-g-14-laion2B-s12B-b42K↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-g/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the stability.ai cluster.

• CLIP-ViT-B-32-DataComp.XL-s13B-b90K↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-B/32 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip). 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.

• CLIP-convnext_base_w-laion2B-s13B-b82K-augreg↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A series of CLIP ConvNeXt-Base (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution

• marqo-fashionCLIP↗

  Marqo

  Marqo-FashionCLIP and Marqo-FashionSigLIP outperform the previous state-of-the-art fashion CLIP models (see results below). Marqo-FashionCLIP leverages Generalised Contrastive Learning (GCL) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. The model was fine-tuned from ViT-B-16 (laion2b_s34b_b88k). **Github Page**: Marqo-FashionCLIP The model can be seamlessly used with OpenCLIP by

• MobileCLIP2-B-OpenCLIP↗

  timm

  These weights and model card are adapted from the original Apple model at https://huggingface.co/apple/MobileCLIP2-B. This version uses canonical OpenCLIP configs and weight naming. MobileCLIP2 was introduced in MobileCLIP2: Improving Multi-Modal Reinforced Training (TMLR August 2025 Featured ), by Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T Toshev, Oncel Tuzel, Hadi Pouransari.

• ViT-B-16-SigLIP↗

  timm

  This model has been converted to PyTorch from the original JAX checkpoints in Big Vision. These weights are usable in both OpenCLIP (image + text) and timm (image only). - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. - **Original:** https://github.com/google-research/big_vision - **Dataset:** WebLI - **Papers:** - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343

• CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-B/32 xlm roberta base model trained with the LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the stability.ai cluster.

• ViT-B-16-SigLIP2-512↗

  timm

  This model has been converted for use in OpenCLIP from the original JAX checkpoints in Big Vision. - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. - **Original:** https://github.com/google-research/big_vision - **Dataset:** WebLI - **Papers:** - SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features: https://arxiv.org/abs/2502.14786 - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343