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MODEL Listed

CLIP convnext base w laion2B s13B b82K augreg

This model is a high-performance vision-language encoder based on the ConvNeXt architecture, trained on the massive LAION-2B dataset. Unlike traditional ViT-based CLIP models, it leverages a pure convolutional backbone to extract spatial features, often providing better inductive biases for image recognition tasks. It is specifically optimized for image-text retrieval, zero-shot classification, and semantic search. For developers, this means a robust tool for building visual search engines or content moderation systems where precise alignment between natural language queries and image embeddings is critical. Integration is straightforward via standard CLIP interfaces, offering a competitive alternative to Transformer-based encoders when deployment efficiency or specific spatial feature extraction is required.

laionimage-text-retrieval
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01 / MODEL CARD

Model card

This model is a high-performance vision-language encoder based on the ConvNeXt architecture, trained on the massive LAION-2B dataset. Unlike traditional ViT-based CLIP models, it leverages a pure convolutional backbone to extract spatial features, often providing better inductive biases for image recognition tasks. It is specifically optimized for image-text retrieval, zero-shot classification, and semantic search. For developers, this means a robust tool for building visual search engines or content moderation systems where precise alignment between natural language queries and image embeddings is critical. Integration is straightforward via standard CLIP interfaces, offering a competitive alternative to Transformer-based encoders when deployment efficiency or specific spatial feature extraction is required.

Model typeimage-text-retrieval
Providerlaion
Licensemit
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
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Source repositoryhttps://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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