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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.

Image classification
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This is an image classification model based on **Google EfficientNet-B0**, fine-tuned on a subset of the subset of HuggingFace/finepdfs to classify document figures into one of the following 26 categories: 1. **logo** 2. **photograph** 3. **icon** 4. **engineering_drawing** 5. **line_chart** 6. **bar_chart** 7. **other** 8. **table** 9. **flow_chart** 10. **screenshot_from_computer** 11. **signature** 12. **screenshot_from_manual** 13. **geographical_map** 14. **pie_chart** 15. **page_thumbnail** 16. **stamp** 17. **music** 18. **calendar** 19. **qr_code** 20. **bar_code** 21. **full_page_image** 22. **scatter_plot** 23. **chemistry_structure** 24. **topographical_map** 25. **crossword_puzzle** 26. **box_plot** **Note:** This model uses the same architecture and implementation as v2.0. The improved performance is achieved by training on a dataset that is 10 times larger than the one used for v2.0.

Image classification
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