wd-v1-4-swinv2-tagger-v2
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-v1-4-swinv2-tagger-v2 model is an AI image tagging system developed by SmilingWolf. It is capable of identifying ratings, characters, and general tags in images. The model was trained on a dataset of Danbooru images, with a focus on those with at least 10 general tags. It uses the SwinV2 architecture and was trained using TPUs provided by the TRC program.
Compared to similar models like the wd-v1-4-moat-tagger-v2, the wd-v1-4-swinv2-tagger-v2 model has slightly different performance, with a precision-recall threshold of 0.3771 and an F1 score of 0.6854. The wd-v1-4-moat-tagger-v2 model has a slightly higher F1 score of 0.6911.
Model inputs and outputs
Inputs
- Images of various subjects and styles
Outputs
- Tags for the image, including ratings, characters, and general tags
- Confidence scores for each tag
Capabilities
The wd-v1-4-swinv2-tagger-v2 model can accurately identify a wide range of tags in images, from character names to general descriptors. This can be useful for organizing and categorizing large image collections, as well as for providing relevant information to users.
What can I use it for?
The wd-v1-4-swinv2-tagger-v2 model could be used in a variety of applications, such as:
- Building image search and discovery tools
- Automating the tagging and categorization of image libraries
- Providing contextual information to users viewing images
- Integrating image understanding capabilities into other software systems
By using the model's outputs, developers can create powerful image-based applications that leverage the model's ability to accurately identify and describe the contents of images.
Things to try
One interesting thing to try with the wd-v1-4-swinv2-tagger-v2 model is to use it in conjunction with other AI models, such as text-to-image generation models. By combining the image tagging capabilities of this model with the image generation abilities of other models, you could create novel applications that allow users to explore and create visually rich content.
Another idea is to fine-tune the model on a specialized dataset to improve its performance on specific types of images or tags. This could be particularly useful for applications that require highly accurate tagging in niche domains.
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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wd-v1-4-vit-tagger-v2
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wd-v1-4-vit-tagger-v2 is an AI model developed by SmilingWolf that supports rating, character, and general tag classification for images. It was trained on Danbooru images using the SmilingWolf/SW-CV-ModelZoo project, with TPU support provided by the TRC program. Similar models include the wd-v1-4-swinv2-tagger-v2, wd-vit-tagger-v3, and wd-v1-4-moat-tagger-v2. Model inputs and outputs Inputs Image data Outputs Image tags for ratings, characters, and general tags Capabilities The wd-v1-4-vit-tagger-v2 model can classify images with tags for ratings, characters, and general topics. It was trained on a large dataset of Danbooru images and achieves an F1 score of 0.6770 on the validation set. What can I use it for? You can use wd-v1-4-vit-tagger-v2 to automatically tag images with relevant metadata, which could be useful for organizing and categorizing large image collections. The model could also be applied to tasks like content moderation, where it could identify and flag inappropriate or sensitive content. Things to try One interesting thing to try with wd-v1-4-vit-tagger-v2 would be to explore how its performance compares to the similar models developed by SmilingWolf, such as the wd-v1-4-swinv2-tagger-v2 and wd-vit-tagger-v3 models. This could provide insights into the relative strengths and weaknesses of different architectural choices for image classification tasks.
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wd-swinv2-tagger-v3
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The wd-swinv2-tagger-v3 is an AI model developed by SmilingWolf that supports ratings, characters and general tags. It is trained on Danbooru images using the JAX-CV framework and TPUs provided by the TRC program. This model is part of a series of image tagging models created by SmilingWolf, including the wd-vit-tagger-v3, wd-vit-large-tagger-v3, wd-v1-4-swinv2-tagger-v2, wd-v1-4-vit-tagger-v2, and wd-v1-4-moat-tagger-v2. Model inputs and outputs The wd-swinv2-tagger-v3 model takes an image as input and outputs a set of predicted tags, including ratings, characters, and general tags. The model was trained on a curated dataset of Danbooru images, filtering out low-quality images and infrequent tags. Inputs Image Outputs Predicted tags for the input image, including ratings, characters, and general tags Capabilities The wd-swinv2-tagger-v3 model can accurately predict a wide range of tags for images, including ratings, characters, and general tags. It has been validated to achieve a macro-F1 score of 0.4541 on a held-out test set. This model can be useful for applications such as content moderation, image organization, and visual search. What can I use it for? The wd-swinv2-tagger-v3 model can be used in a variety of applications that involve image tagging and classification. For example, it could be used to automatically tag and organize large image collections, enabling more efficient search and retrieval. It could also be used for content moderation, helping to identify and filter out images with inappropriate or explicit content. Things to try One interesting aspect of the wd-swinv2-tagger-v3 model is its ability to handle class imbalance in the training data. The maintainer used tag frequency-based loss scaling to address this issue, which can be a useful technique for other image tagging tasks with skewed label distributions. Developers could experiment with this approach or explore other methods for dealing with class imbalance when working with the model.
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wd-vit-tagger-v3
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The wd-vit-tagger-v3 is an AI model developed by SmilingWolf that supports ratings, characters, and general tags. It was trained using the JAX-CV framework, with TPU training provided by the TRC program. The model builds upon previous versions, with improvements such as more training data, updated tags, and ONNX compatibility. Compared to similar models like the WD 1.4 SwinV2 Tagger V2 and WD 1.4 MOAT Tagger V2 from the same maintainer, the wd-vit-tagger-v3 model uses a Vision Transformer (ViT) architecture and includes additional training and dataset improvements. Model inputs and outputs Inputs Images of various dimensions Outputs Ratings, characters, and general tags associated with the input image Capabilities The wd-vit-tagger-v3 model is capable of accurately predicting a wide range of tags for images, including ratings, characters, and general tags. It has shown strong performance on the validation dataset, with a Macro-F1 score of 0.4402. What can I use it for? The wd-vit-tagger-v3 model can be used for a variety of image-to-text tasks, such as automatically tagging and categorizing images in a database or content moderation. Its ability to predict a diverse set of tags makes it useful for applications that require detailed metadata about images, like content recommendation systems or visual search engines. Things to try One interesting aspect of the wd-vit-tagger-v3 model is its ONNX compatibility, which allows for efficient batch inference. Developers can leverage this to build high-performance image tagging pipelines that can process large volumes of images. Additionally, the model's performance on the validation dataset suggests it may be a good starting point for fine-tuning on domain-specific datasets, potentially leading to even more accurate and specialized image tagging capabilities.
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wd-v1-4-moat-tagger-v2
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wd-v1-4-moat-tagger-v2 is an AI model developed by SmilingWolf that can generate image tags and ratings. It was trained on a dataset of Danbooru images and can produce both general tags and character tags. The model is similar to wd-v1-4-vit-tagger in its tagging capabilities, and another related model is Kohaku-XL-Delta, which is a text-to-image model. Model inputs and outputs wd-v1-4-moat-tagger-v2 takes an image as input and outputs a set of tags and ratings that describe the contents of the image. The model was trained on a filtered subset of the Danbooru dataset, removing images with fewer than 10 general tags. Inputs Image**: The model takes an image as input and generates tags and ratings that describe its contents. Outputs Ratings**: The model outputs ratings such as "masterpiece", "best quality", "good quality", etc. based on the image's perceived quality. Characters**: The model identifies characters present in the image and outputs their names as tags. General tags**: The model generates a set of general tags that describe the contents of the image, such as objects, scenes, and visual attributes. Capabilities wd-v1-4-moat-tagger-v2 can effectively tag and rate a wide variety of anime-style images. It has been trained on a large dataset and can identify a broad range of characters, objects, and visual elements. The model demonstrates strong performance, with a reported F1 score of 0.6911 on the validation set. What can I use it for? You can use wd-v1-4-moat-tagger-v2 to automatically generate metadata and tags for your anime-style image collections. This could be useful for organizing and searching your images, or for providing detailed descriptions to accompany your artwork. The model's ratings could also be helpful for filtering or curating images based on quality. Things to try One interesting aspect of wd-v1-4-moat-tagger-v2 is its ability to identify a large number of characters. You could experiment with using the model to automatically suggest character tags for your images, which could save time and ensure consistent tagging. Additionally, you could explore how the model's ratings correlate with human perceptions of image quality, and use this information to refine your image curation process.
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