Instructions to use umm-maybe/AI-image-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use umm-maybe/AI-image-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="umm-maybe/AI-image-detector") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("umm-maybe/AI-image-detector", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesThis app is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI.
It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It may nonetheless be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors.
Furthermore the intended scope of this tool is artistic images; that is to say, it is not a deepfake photo detector, and general computer imagery (webcams, screenshots, etc.) may throw it off.
In general, this tool can only serve as one of many potential indicators that an image was AI-generated. Images scoring as very probably artificial (e.g. 90% or higher) could be referred to a human expert for further investigation, if needed.
For more information please see the blog post describing this project at:
https://medium.com/@matthewmaybe/can-an-ai-learn-to-identify-ai-art-545d9d6af226
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# Model Trained Using AutoTrain
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# Model Trained Using AutoTrain
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