Convnextv2 Tiny.fcmae Ft In22k In1k Alternatives

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.

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 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.

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.

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.

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.

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.

convnextv2-tiny-1k-224 is a tiny variant of the ConvNeXt V2 vision model trained on ImageNet-1K at 224x224 resolution. Developed by Meta AI, it offers strong performance on image classification tasks while maintaining a compact size. The model is available in multiple frameworks including PyTorch and TensorFlow and can be used via the Hugging Face Transformers pipeline.

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.

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.

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.

Convnext Base.dinov3 Lvd1689m is an image feature extraction model hosted on Hugging Face. It belongs to the ConvNeXt Base architecture and carries the dinov3 pretraining. The model is provided by the timm PyTorch Image Models collection and is made available for use in computer vision pipelines that require feature representations from images. It can be loaded directly through the timm library using the command timm.create_model with the identifier hf_hub:timm/convnext_base.dinov3_lvd1689m and the pretrained flag set to true. The same model is also accessible via the Hugging Face Transformers library, either through a high-level pipeline tagged for image-feature-extraction or by instantiating an AutoModel with the repository name timm/convnext_base.dinov3_lvd1689m and an optional device_map. Model weights are distributed in both PyTorch and Safetensors formats. The model card references two arXiv papers, 2508.10104 and 2201.03545, that document the underlying methods. It carries the dinov3-license. The entry appears under the Image Feature Extraction task category on the platform. No pricing information is listed because the model is offered as an open download from the public repository.