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ai_vs_real_image_detection
dima806Beware that this model is trained on a dataset collected about 2 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.
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Returns person celebrity-style attractiveness (0 to 1) based on facial image with about 83% accuracy.
car_models_image_detection
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closed_eyes_image_detection
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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.
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email-spam-detection-roberta
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facial_age_image_detection
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fairface_age_image_detection
dima806See https://www.kaggle.com/code/dima806/age-group-image-classification-vit for details.
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hair_type_image_detection
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Returns with about 98.7% accuracy whether the face belongs to man or woman based on face image. See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details.
music_genres_classification
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skin_types_image_detection
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