Update README.md

README.md

@@ -22,62 +22,6 @@ from huggingface_hub import from_pretrained_keras
 model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
 }}
 
-{{
-#Required imports and Huggingface authentication
-import os
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-os.environ["SM_FRAMEWORK"] = "tf.keras"
-import segmentation_models as sm
-import tensorflow as tf
-from huggingface_hub import from_pretrained_keras
-from huggingface_hub import notebook_login
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-weights = [0.5,0.5] # hyper parameter
-
-dice_loss = sm.losses.DiceLoss(class_weights = weights)
-focal_loss = sm.losses.CategoricalFocalLoss()
-TOTAL_LOSS_FACTOR = 5
-total_loss = dice_loss + (TOTAL_LOSS_FACTOR * focal_loss)
-
-def jaccard_coef(y_true, y_pred):
-    """
-    Defines custom jaccard coefficient metric
-    """
-    
-    y_true_flatten = K.flatten(y_true)
-    y_pred_flatten = K.flatten(y_pred)
-    intersection = K.sum(y_true_flatten * y_pred_flatten)
-    final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
-    return final_coef_value
-
-metrics = [tf.keras.metrics.MeanIoU(num_classes=2, sparse_y_true= False, sparse_y_pred=False, name="Mean IOU")]
-notebook_login() 
-
-#Load model from Huggingface Hub
-model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
-model.compile(metrics=metrics)
-
-#Inference -- User needs to specify the image path where label and ash images are stored
-label = np.load({Image path} + 'human_pixel_masks.npy')
-ash_image = np.load({Image path} + 'ash_image.npy')[...,4] 
-y_pred = model.predict(ash_image.reshape(1,256, 256, 3))
-prediction = np.argmax(y_pred[0], axis=2).reshape(256,256,1)
-
-fig, ax = plt.subplots(1, 2, figsize=(9, 5))
-fig.tight_layout(pad=5.0)
-ax[1].set_title("Contrail prediction")
-ax[1].imshow(ash_image)
-ax[1].imshow(prediction)
-ax[1].axis('off')
-
-ax[0].set_title("False colored satellite image")
-ax[0].imshow(ash_image)
-ax[0].axis('off')}}
-
 
 ## Training and evaluation data