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AI モデル 76

• vit_base_patch32_clip_224.laion400m_e32↗

  timm

  This model is a dual use `open_clip` and `timm` model. Model name in OpenCLIP is `ViT-B-32-quickgelu`, and timm name is `vit_base_patch32_clip_224.laion400m_e32`.

• DFN2B-CLIP-ViT-L-14↗

  apple

  A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-2B. Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. This model was trained on 2B images that were filtered from a pool of 12.8B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B). This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). These weights are directly usable in OpenCLIP (image + text).

• CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A series of CLIP ConvNeXt-Large (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of LAION-5B using OpenCLIP. The models utilize: * the timm ConvNeXt-Large model (`convnext_large`) as the image tower * a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models * a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768).

• ViT-B-32-SigLIP2-256↗

  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

• MobileCLIP2-S3-OpenCLIP↗

  timm

• ViT-L-16-SigLIP2-256↗

  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

• bioclip↗

  imageomics

  BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date. Through rigorous benchmarking on a diverse set of fine-grained biological classification tasks, BioCLIP consistently outperformed existing baselines by 16% to 17% absolute. Through intrinsic evaluation, we found that BioCLIP learned a hierarchical representation aligned to the tree of life, which demonstrates its potential for robust generalizability. **See the `examples/` directory for examples of how to use BioCLIP in zero-shot and few-shot settings.** BioCLIP is based on OpenAI's CLIP. We trained the model on TreeOfLife-10M from OpenAI's ViT-B/16 checkpoint, using OpenCLIP's code. BioCLIP is trained with the standard CLIP objective to imbue the model with an understanding, not just of different species, but of the hierarchical structure that relates species across the tree of life. In this way, BioCLIP offers potential to aid biologists in discovery of new and related creatures, since it does not see the 454K different taxa as distinct classes, but as part of an interconnected hierarchy.

• vit_large_patch14_clip_336.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-large-patch14-336 for use in Hugging Face Transformers.

• CLIP-ViT-B-32-256x256-DataComp-s34B-b86K↗

  laion

  1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. How To Get Started With the Model 6. Acknowledgements 7. Citation A CLIP ViT-B/32 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip) at 256x256 resolution. 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-14-SigLIP-384↗

  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