wd-vit-large-tagger-v3
Maintainer: SmilingWolf
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The wd-vit-large-tagger-v3 model is a powerful image tagging AI developed by SmilingWolf. It is capable of accurately identifying ratings, characters, and general tags in images. The model was trained on the Danbooru dataset using JAX-CV, with TPUs provided by the TRC program. This model builds upon previous versions, with more training data, updated tags, and improved performance.
The wd-vit-tagger-v3 and wd-v1-4-vit-tagger-v2 are similar models also created by SmilingWolf. They share the same core capabilities but have slight differences in their training datasets and performance metrics. The wd-v1-4-swinv2-tagger-v2 and wd-v1-4-moat-tagger-v2 models use different architectural approaches, incorporating SwinV2 and MOAT respectively.
Model inputs and outputs
Inputs
- Image: The
wd-vit-large-tagger-v3model takes an image as input and processes it to identify relevant tags.
Outputs
- Tags: The model outputs a set of tags, including ratings, characters, and general tags, along with their corresponding confidence scores.
Capabilities
The wd-vit-large-tagger-v3 model excels at accurately identifying a wide range of tags in images, including ratings, characters, and general tags. It has been trained on a diverse dataset of Danbooru images and can handle a variety of image types and subjects.
What can I use it for?
The wd-vit-large-tagger-v3 model can be used for a variety of applications, such as organizing and categorizing large image collections, powering image search and recommendation systems, and enhancing content moderation tools. Its robust tagging capabilities make it a valuable asset for businesses, researchers, and creators working with visual media.
Things to try
One interesting aspect of the wd-vit-large-tagger-v3 model is its versatility. You can experiment with using it in different contexts, such as applying it to your own image datasets or integrating it into larger computer vision pipelines. The provided inference code examples, ONNX model, and JAX implementation offer a great starting point for exploring the model's capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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