vit-age-classifier
Maintainer: nateraw
86
🛠️
| 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 vit-age-classifier is a Vision Transformer (ViT) model that has been fine-tuned to classify the age of a person's face in an image. This model builds upon the Vision Transformer (base-sized model) and the Vision Transformer (base-sized model) pre-trained on ImageNet-21k, which are general-purpose pre-trained image classification models. The vit-age-classifier model has been further trained on a proprietary dataset of facial images to specialize in age prediction.
Similar models include the Fine-Tuned Vision Transformer (ViT) for NSFW Image Classification, which can be used for content moderation, and the CLIP model, which can be used for zero-shot image classification. However, the vit-age-classifier is unique in its specialization for facial age prediction.
Model inputs and outputs
Inputs
- Image: The model takes a single image as input, which should contain a human face.
Outputs
- Age prediction: The model outputs a predicted age for the person in the input image.
Capabilities
The vit-age-classifier model can be used to accurately predict the age of a person's face in an image. This can be useful for applications such as age-based content filtering, demographic analysis, or user interface customization. The model has been trained on a diverse dataset, so it should perform well on a variety of facial images.
What can I use it for?
The vit-age-classifier model could be used in a variety of applications that require age-based analysis of facial images. For example, it could be integrated into a content moderation system to filter out age-inappropriate content, or used to provide age-targeted recommendations in a media platform. It could also be used to analyze demographic trends in a dataset of facial images.
To use the model, you can load it directly from the Hugging Face model hub using the provided code examples. You can then pass in new facial images and get age predictions for the people in those images.
Things to try
One interesting thing to try with the vit-age-classifier model would be to evaluate its performance on a diverse dataset of facial images, including people of different ages, genders, and ethnicities. This could help understand any potential biases or limitations in the model's predictions.
You could also try fine-tuning the model on your own dataset of facial images to see if you can improve its accuracy for your specific use case. The provided code examples should give you a good starting point for integrating the model into your own applications.
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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