Update README.md

README.md

@@ -7,4 +7,26 @@ metrics:
 - f1
 - precision
 ---
-Dataset: https://www.kaggle.com/datasets/jarricgentletail/mobilenetv3-preprocessed-orange-disease-fruit-dset
+Dataset: https://www.kaggle.com/datasets/jarricgentletail/mobilenetv3-preprocessed-orange-disease-fruit-dset
+
+Download model and to run use the following code:
+```Python
+torch.serialization.add_safe_globals([MobileNetV3])
+torch.serialization.add_safe_globals([Sequential])
+torch.serialization.add_safe_globals([Conv2dNormActivation])
+torch.serialization.add_safe_globals([Conv2d])
+torch.serialization.add_safe_globals([BatchNorm2d])
+torch.serialization.add_safe_globals([Hardswish])
+torch.serialization.add_safe_globals([InvertedResidual])
+torch.serialization.add_safe_globals([ReLU])
+torch.serialization.add_safe_globals([SqueezeExcitation])
+torch.serialization.add_safe_globals([AdaptiveAvgPool2d])
+torch.serialization.add_safe_globals([Hardsigmoid])
+torch.serialization.add_safe_globals([Linear])
+torch.serialization.add_safe_globals([Dropout])
+
+mobilenetv3 = torch.load("/home/jarric/PycharmProjects/OrangeRecognizer/outputs/2025-04-21 12:08:02.020215/model_99_finetuned.pt")
+```
+
+I kept saving whole class instead of just the state dict. 
+The model was finetuned, based on IMAGENETV1 dataset. I just finetuned the classification head.