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README.md
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license: mit
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license: mit
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FLUX Detection model with sklearn API using wavelets and UMAP embeddings and then K-Nearest neighbors for classification.
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The process is as follows: DWT -> UMAP -> KNN. Accuracy varies depending on the dataset but can be anywhere from 80%-90% depending on the dataset you use.
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Methods include fit, predict, and predict_proba. First, load an image using PIL (Pillow) and then store using an array. Load the class using joblib and then predict.
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The model is already pretrained but can be trained again using fit.
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```python
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model = joblib.load("flux_classifier.pkl")
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images = [Image.open("image.jpeg")]
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predictions = model.predict(images)
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```
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