Convnext Base.dinov3 Lvd1689m Alternatives

This is a ConvNeXt Base model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at 384x384 resolution. It is part of the timm (PyTorch Image Models) library and available on Hugging Face for use with Transformers or timm. It is designed for developers building computer vision applications.

The timm/convnext_base.fb_in22k_ft_in1k is a ConvNeXt Base model for image classification. It was pre-trained on the ImageNet-22k dataset and fine-tuned on ImageNet-1k, as indicated by its name and associated tags. The model forms part of the timm PyTorch Image Models collection hosted on Hugging Face. It can be loaded and used through two primary libraries. With timm, the model is instantiated via a single create_model call that references the Hugging Face hub identifier and enables pretrained weights. The Transformers library supports it through a high-level pipeline configured for image-classification tasks as well as direct loading of the underlying model with AutoModel, including options for automatic device mapping. Example code demonstrates inference on an image URL such as a photograph of parrots. The model card lists it under the Apache-2.0 license. It includes PyTorch and Safetensors file formats along with references to the arXiv paper 2201.03545. The entry appears in the context of image classification tasks and carries tags for both imagenet-1k and imagenet-22k datasets. This implementation is delivered as a downloadable model repository on the Hugging Face platform rather than a standalone application. It targets developers and researchers who integrate it into Python-based computer vision workflows for inference, further fine-tuning, or as a feature extractor.

Convnext Small.fb In22k Ft In1k 384 is an image classification model hosted on Hugging Face as part of the timm PyTorch Image Models collection. It carries the identifier timm/convnext_small.fb_in22k_ft_in1k_384 and was trained on the imagenet-22k and imagenet-1k datasets. The model follows the architecture described in the paper with arxiv identifier 2201.03545. It is delivered as a PyTorch model with Safetensors weights and supports integration through the timm library or the Hugging Face Transformers library. Code examples show creation via timm.create_model with the pretrained flag set to true, as well as loading through the Transformers pipeline for image-classification tasks or directly with AutoModel.from_pretrained. The repository provides instructions for use with libraries, inference providers, notebooks, and local applications. The model is licensed under apache-2.0. It is maintained under the PyTorch Image Models organization on Hugging Face, which has accumulated 890 likes for the associated collection. No pricing information is stated because the model is openly available for download and local or hosted inference.

This is a Hugging Face repository hosting the DINOv3 ViT-L/16 pretrained model (arxiv:2508.10104). It provides a vision transformer trained using self-supervised learning for high-quality image feature extraction. The model can be used with the Transformers library for downstream computer vision tasks including classification, retrieval, and segmentation. It is intended for ML practitioners building on top of strong visual representations.

This is a DINOv3 vision transformer (ViT-S/16+) model pre-trained by Meta AI on the large-scale LVD-1689M dataset using self-supervised learning. It excels at producing high-quality image embeddings and features suitable for downstream computer vision tasks such as classification, retrieval, and segmentation. The model is accessible via the Hugging Face Transformers library.

This is a ConvNeXt Femto model trained on ImageNet-1K, made available through the timm (PyTorch Image Models) library. It supports both timm and Hugging Face Transformers interfaces for image classification and feature extraction. The model is optimized for speed and efficiency while maintaining competitive accuracy.

ConvNeXt Tiny is a modernized convolutional neural network from the ConvNeXt family. This variant is pretrained on ImageNet-22K and fine-tuned on ImageNet-1K. It is accessible via the timm library and Hugging Face Transformers for image classification tasks.

Convnextv2 Tiny.fcmae Ft In22k In1k is an image classification model hosted on Hugging Face as part of the PyTorch Image Models collection. It belongs to the ConvNeXtV2 family and carries the specific identifier convnextv2_tiny.fcmae_ft_in22k_in1k, indicating pre-training on ImageNet-22k followed by fine-tuning on ImageNet-1k. The model is provided under the cc-by-nc-4.0 license and is associated with the paper arxiv:2301.00808. It can be loaded and run through the timm library using the call timm.create_model with the pretrained flag set to true. The same model is also accessible via the Hugging Face Transformers library, where users may instantiate it directly with AutoModel.from_pretrained or employ the high-level pipeline for image-classification tasks on input images such as remote URLs. Files are distributed in both PyTorch and Safetensors formats. The model is listed under the Image Classification task and supports inference through standard library integrations. It forms one entry in a broader repository of models maintained under the timm organization on Hugging Face.

Convnext Tiny.in12k Ft In1k is an image classification model hosted on Hugging Face. It belongs to the ConvNeXt family and carries the specific identifier convnext_tiny.in12k_ft_in1k, indicating pre-training on the ImageNet-12k dataset followed by fine-tuning on ImageNet-1k. The model is provided by the timm library, also known as PyTorch Image Models. It is distributed with safetensors support and carries an Apache-2.0 license. Users can load it through the timm library by calling timm.create_model with the identifier "hf_hub:timm/convnext_tiny.in12k_ft_in1k" and pretrained set to True. It is also compatible with the Hugging Face Transformers library, where it can be instantiated via a pipeline for image-classification tasks or loaded directly with AutoModel.from_pretrained. The model card references the paper arxiv:2201.03545. Example inference code demonstrates classification on an image URL such as a parrots.png sample from the documentation dataset. Notebooks for Google Colab and Kaggle are linked for further experimentation. It is made available as an open model on the Hugging Face platform for download and integration into PyTorch-based workflows.

Convnext Small.in12k Ft In1k is an image classification model hosted on Hugging Face as part of the timm PyTorch Image Models collection. It was pretrained on the ImageNet-12k dataset and fine-tuned on ImageNet-1k, as indicated by its name and associated tags. The model carries an arxiv reference to paper 2201.03545. It can be loaded and run through the timm library by calling timm.create_model with the identifier "hf_hub:timm/convnext_small.in12k_ft_in1k" and pretrained set to True. The same model is also accessible via the Hugging Face Transformers library, either through a high-level image-classification pipeline that accepts image URLs or by directly instantiating it with AutoModel.from_pretrained, including support for automatic device mapping. Example code demonstrates inference on a sample parrot image from the Hugging Face documentation dataset. The model is provided in PyTorch and Safetensors formats. It is published under the Apache-2.0 license. No pricing information is stated because the model is available for direct download and local or hosted use through the open Hugging Face platform.

The facebook/dinov3-vith16plus-pretrain-lvd1689m model is a self-supervised vision transformer available on Hugging Face. It belongs to the DINOv3 family and carries the specific ViT-H/16+ architecture. The model was pre-trained on the LVD-1689M dataset and addresses the need for general-purpose image representations that can be transferred to downstream computer vision tasks without task-specific labeled data. It performs image feature extraction. The model card lists it under the image-feature-extraction task tag and supplies example code that loads it through the Transformers pipeline for that purpose. Integration with the Hugging Face ecosystem occurs via the Transformers library, where users instantiate an AutoImageProcessor and an AutoModel. The repository provides both the model weights in Safetensors format and the associated code for direct loading or pipeline usage. Notebooks for Google Colab and Kaggle are referenced to support experimentation. The model originates from AI at Meta. Its paper is identified by the arXiv identifier 2508.10104. The license is explicitly named dinov3-license. The repository itself is hosted on Hugging Face, allowing deployment through standard inference providers or local applications that support the Transformers library.

This is the DINOv3 ViT-S/16 model pretrained on a large dataset (LVD-1689M). It learns powerful image features through self-supervised learning. The model can be used with the Transformers library for feature extraction and is suitable as a backbone for various computer vision applications.

This is a ConvNeXt Small model pretrained on ImageNet-22k and fine-tuned on ImageNet-1k, provided by the PyTorch Image Models (timm) library. It is available on Hugging Face for easy integration with Transformers or timm. The model is designed for image classification and supports standard inference pipelines.

Dinov2 Base is a base-sized vision transformer model trained using DINOv2 for image feature extraction. Hosted on Hugging Face by AI at Meta, it belongs to the class of vision transformers and is distributed with open weights under the Apache-2.0 license. The model supports direct use through the Transformers library. Code examples show loading it via a high-level pipeline for image-feature-extraction or by importing AutoImageProcessor and AutoModel from_pretrained with the identifier facebook/dinov2-base. It is provided in PyTorch and Safetensors formats. Intended for computer vision researchers and developers, the model is accompanied by a model card that includes a description, intended uses and limitations, how-to-use guidance, and citation information referencing arXiv:2304.07193. It can be deployed through Hugging Face inference providers, notebooks such as Google Colab and Kaggle, or local applications. The repository lists it among collections for image feature extraction and includes community contributions along with options to copy the model to a bucket or follow updates from the maintainer.

ConvNeXt V2 Tiny is a modernized convolutional neural network architecture developed by Meta AI. This variant was pretrained on the ImageNet-22K dataset at 224x224 resolution. It can be used for image classification and as a strong backbone for downstream computer vision tasks. The model is available through both the timm library and Hugging Face Transformers.

This model is a ConvNeXt Base image encoder trained with CLIP on the LAION-2B dataset and provided through the timm library. It generates rich feature embeddings suitable for image-text similarity, zero-shot classification, and retrieval. It integrates with both timm and Hugging Face Transformers for easy use in computer vision pipelines.

This ConvNeXt Base model has been pre-trained using CLIP on LAION-2B data and fine-tuned on ImageNet-12k and ImageNet-1k. It is part of the PyTorch Image Models (timm) library and can be used for image classification and as a feature extractor. The model offers strong performance on standard vision benchmarks.

Vit Base Patch14 Reg4 Dinov2.lvd142m is a pretrained model hosted on Hugging Face for image feature extraction. It belongs to the class of vision transformer models and was developed within the timm PyTorch Image Models library. The model carries an apache-2.0 license and is made available as an open artifact for integration into computer vision workflows. It can be loaded directly through the timm library by calling timm.create_model with the identifier "hf_hub:timm/vit_base_patch14_reg4_dinov2.lvd142m" and the pretrained flag set to true. The same checkpoint supports the Transformers library, where developers instantiate it via AutoModel.from_pretrained or through a pipeline configured for the image-feature-extraction task. These loading paths enable use in notebooks such as Google Colab or Kaggle as well as in local applications and inference providers. The model card references three arXiv papers that document the underlying techniques. Its primary output consists of image representations suitable for downstream tasks, though no additional training details or performance metrics appear on the page. The repository provides safetensors and PyTorch format files along with version history.

Vit Base Patch14 Dinov2.lvd142m is a pre-trained image feature extraction model hosted on Hugging Face. It belongs to the class of Vision Transformer models and was developed within the timm PyTorch Image Models library. The model carries the apache-2.0 license and draws on research described in arxiv papers 2304.07193 and 2010.11929. It supports direct use through the timm library by creating the model with a single line that loads pretrained weights from the Hugging Face hub. The same weights are also accessible via the Transformers library, where developers can instantiate it with AutoModel or employ the image-feature-extraction pipeline for straightforward inference. Model files are provided in both PyTorch and Safetensors formats. The entry appears under the Image Feature Extraction task tag. It is delivered exclusively as a downloadable model artifact rather than a standalone application, allowing integration into notebooks, local scripts, or inference services that support timm or Transformers. No pricing information is listed because the model is fully open source.

DINOv2 with registers is a base-sized Vision Transformer trained using the DINOv2 self-supervised method by Meta. It produces powerful image representations suitable for feature extraction, classification, and retrieval tasks. The model is available through the Hugging Face Transformers library.