サイト内検索
AI モデル 76
- CLIP-ViT-B-16-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/16 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.
- PE-Core-B-16↗
timm
This is an OpenCLIP (image + text) remaped version of the the original Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "Perception Encoder: The best visual embeddings are not at the output of the network". **Model Overview**: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features.
- vit_base_patch16_plus_clip_240.laion400m_e31↗
timm
This model is a dual use `open_clip` and `timm` model. Model name in OpenCLIP is `ViT-B-16-plus-240`, and timm name is `vit_base_patch16_plus_clip_240.laion400m_e31`.
- convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384↗
timm
A ConvNeXt image classification model. CLIP image tower weights pretrained in OpenCLIP on LAION and fine-tuned on ImageNet-12k followed by ImageNet-1k in `timm` bby Ross Wightman. Please see related OpenCLIP model cards for more details on pretrain: * https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup * https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg * https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg * https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
- ViT-B-16-SigLIP2__webli↗
immich-app
This repo contains ONNX exports for the associated CLIP model by OpenCLIP. See the OpenCLIP repo for more info.
- vit_large_patch14_clip_224.metaclip_2pt5b↗
timm
This model is a dual use `open_clip` and `timm` model. Model name in OpenCLIP is `ViT-L-14-quickgelu`, and timm name is `vit_large_patch14_clip_224.metaclip_2pt5b`.
- ViT-B-16-SigLIP2↗
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
- vit_base_patch16_clip_224.openai↗
timm
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. This instance of the CLIP model is intended for loading in * `timm` (https://github.com/rwightman/pytorch-image-models) and * `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries. Please see https://huggingface.co/openai/clip-vit-base-patch16 for use in Hugging Face Transformers.
- CLIP-ViT-H-14-laion2B-s32B-b79K↗
hoaiht
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-H/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). 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.
- ViT-SO400M-16-SigLIP2-384__webli↗
immich-app
This repo contains ONNX exports for the associated CLIP model by OpenCLIP. See the OpenCLIP repo for more info.