nsfw_image_detection
Maintainer: lucataco
4.5K
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The nsfw_image_detection model is a fine-tuned Vision Transformer (ViT) developed by Falcons.ai for detecting NSFW (Not Safe For Work) content in images. This model is similar to other Vision-Language models created by the same maintainer, such as DeepSeek-VL, PixArt-XL, and RealVisXL-V2.0. These models aim to provide robust visual understanding capabilities for real-world applications.
Model inputs and outputs
The nsfw_image_detection model takes a single input - an image file. The model will then output a string indicating whether the image is "normal" or "nsfw".
Inputs
- image: The input image file to be classified.
Outputs
- Output: A string indicating whether the image is "normal" or "nsfw".
Capabilities
The nsfw_image_detection model is capable of detecting NSFW content in images with a high degree of accuracy. This can be useful for a variety of applications, such as content moderation, filtering inappropriate images, or ensuring safe browsing experiences.
What can I use it for?
The nsfw_image_detection model can be used in a wide range of applications that require the ability to identify NSFW content in images. For example, it could be integrated into a social media platform to automatically flag and remove inappropriate content, or used by a parental control software to filter out unsuitable images. Companies looking to monetize this model could explore integrating it into their content moderation solutions or offering it as a standalone API to other businesses.
Things to try
One interesting thing to try with the nsfw_image_detection model is to experiment with its performance on a variety of image types, including artistic or ambiguous content. This could help you understand the model's limitations and identify areas for potential improvement. Additionally, you could try combining this model with other computer vision models, such as GFPGAN for face restoration, or Vid2OpenPose for pose estimation, to create more sophisticated multimedia processing pipelines.
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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