dima806

Detects age group with about 59% accuracy based on an image. See https://www.kaggle.com/code/dima806/age-group-image-classification-vit for details.

--- license: apache-2.0 metrics: - accuracy - roc_auc base_model: - facebook/wav2vec2-base-960h --- [Music genre](https://en.wikipedia.org/wiki/Music_genre) classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include:

Returns facial emotion with about 91% accuracy based on facial human image.

Checks whether an image is real or fake (AI-generated). Note to users who want to use this model in production Beware that this model is trained on a dataset collected about 3 years ago. Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift. To mitigate that, I urge you to retrain the model using the latest available labeled data. As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice. However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift. See https://www.kaggle.com/code/dima806/deepfake-vs-real-faces-detection-vit for more details.

See https://www.kaggle.com/code/dima806/facial-age-years-detection-vit for more details.

Returns car brand with about 69% accuracy given an image. See https://www.kaggle.com/code/dima806/car-brands-image-detection-vit for details.