--- inference: false license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: AI-generated_images_detector results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9735697557711609 --- # AI-generated_images_detector This model achieves the following results on the evaluation set: - Loss: 0.0987 - Accuracy: 0.9736 # To utilize this model ``` python from PIL import Image from transformers import pipeline classifier = pipeline("image-classification", model="NYUAD-ComNets/NYUAD_AI-generated_images_detector") image=Image.open("path_to_image") pred=classifier(image) print(pred) ``` ## Training and evaluation data ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0431 | 0.55 | 100 | 0.1672 | 0.9568 | | 0.0139 | 1.1 | 200 | 0.2338 | 0.9398 | | 0.0201 | 1.66 | 300 | 0.1291 | 0.9655 | | 0.0023 | 2.21 | 400 | 0.1147 | 0.9709 | | 0.0033 | 2.76 | 500 | 0.0987 | 0.9736 | # BibTeX entry and citation info ``` @article{aldahoul2024detecting, title={Detecting AI-Generated Images Using Vision Transformers: A Robust Approach for Safeguarding Visual Media Integrity}, author={AlDahoul, Nouar and Zaki, Yasir}, journal={Available at SSRN}, year={2024} } @misc{ComNets, url={https://huggingface.co/NYUAD-ComNets/NYUAD_AI-generated_images_detector](https://huggingface.co/NYUAD-ComNets/NYUAD_AI-generated_images_detector)}, title={NYUAD_AI-generated_images_detector}, author={Nouar AlDahoul, Yasir Zaki} }