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---
tags:
- vision
- image-classification
- autotrain
license: cc-by-4.0
widget:
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 7.940487247386902
---
*__NOTE__:
To detect pictures generated using older models such as VQGAN+CLIP, please use the [updated version](https://huggingface.co/Organika/sdxl-detector) of this detector instead.*
In this model i'm using a ViT model to predict whether an artistic image was generated using AI or not.
The training dataset didn't include any samples generated from Midjourney 5, SDXL, or DALLE-3. But was trained on outputs of their predecessors.
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.
The potential indicator for this tool is to serve to detect whether an image was AI-generated or not.
Images scoring as very probably artificial (e.g. 90% or higher) could be referred to a human expert for further investigation, if needed.
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 151965872
- CO2 Emissions (in grams): 7.9405
## Validation Metrics
- Loss: 0.163
- Accuracy: 0.942
- Precision: 0.938
- Recall: 0.978
- AUC: 0.980
- F1: 0.958
# License Notice
This work is licensed under a [Creative Commons Attribution-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nd/4.0/).
You may distribute and make this model available to others as part of your own web page, app, or service so long as you provide attribution. However, use of this model within text-to-image systems to evade AI image detection would be considered a "derivative work" and as such prohibited by the license terms. |