Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+
+FLUX Detection model with sklearn API using wavelets and UMAP embeddings and then K-Nearest neighbors for classification.
+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.
+
+ 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.
+ The model is already pretrained but can be trained again using fit.
+
+ ```python
+model = joblib.load("flux_classifier.pkl")
+images = [Image.open("image.jpeg")]
+predictions = model.predict(images)
+```