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diff --git a/predict.py b/predict.py
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+# USAGE
+# python predict.py
+
+# import the necessary packages
+from pyimagesearch import config
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import cv2
+import os
+
+def prepare_plot(origImage, origMask, predMask):
+	# initialize our figure
+	figure, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 10))
+	
+	# plot the original image, its mask, and the predicted mask
+	ax[0].imshow(origImage)
+	ax[1].imshow(origMask)
+	ax[2].imshow(predMask)
+	
+	# set the titles of the subplots
+	ax[0].set_title("Image")
+	ax[1].set_title("Original Mask")
+	ax[2].set_title("Predicted Mask")
+	
+	# set the layout of the figure and display it
+	figure.tight_layout()
+	figure.show()
+	
+
+def make_predictions(model, imagePath):
+	# set model to evaluation mode
+	model.eval()
+	
+	# turn off gradient tracking
+	with torch.no_grad():
+		# load the image from disk, swap its color channels, cast it
+		# to float data type, and scale its pixel values
+		image = cv2.imread(imagePath)
+		image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+		image = image.astype("float32") / 255.0
+		
+		# resize the image and make a copy of it for visualization
+		image = cv2.resize(image, (128, 128))
+		orig = image.copy()
+		
+		# find the filename and generate the path to ground truth
+		# mask
+		filename = imagePath.split(os.path.sep)[-1]
+		groundTruthPath = os.path.join(config.MASK_DATASET_PATH,
+			filename)
+		
+		# load the ground-truth segmentation mask in grayscale mode
+		# and resize it
+		gtMask = cv2.imread(groundTruthPath, 0)
+		gtMask = cv2.resize(gtMask, (config.INPUT_IMAGE_HEIGHT,
+			config.INPUT_IMAGE_HEIGHT))
+		
+        		# make the channel axis to be the leading one, add a batch
+		# dimension, create a PyTorch tensor, and flash it to the
+		# current device
+		image = np.transpose(image, (2, 0, 1))
+		image = np.expand_dims(image, 0)
+		image = torch.from_numpy(image).to(config.DEVICE)
+		
+		# make the prediction, pass the results through the sigmoid
+		# function, and convert the result to a NumPy array
+		predMask = model(image).squeeze()
+		predMask = torch.sigmoid(predMask)
+		predMask = predMask.cpu().numpy()
+		
+		# filter out the weak predictions and convert them to integers
+		predMask = (predMask > config.THRESHOLD) * 255
+		predMask = predMask.astype(np.uint8)
+		
+		# prepare a plot for visualization
+		prepare_plot(orig, gtMask, predMask)
+		
+# load the image paths in our testing file and randomly select 10
+# image paths
+print("[INFO] loading up test image paths...")
+imagePaths = open(config.TEST_PATHS).read().strip().split("\n")
+imagePaths = np.random.choice(imagePaths, size=10)
+# load our model from disk and flash it to the current device
+print("[INFO] load up model...")
+unet = torch.load(config.MODEL_PATH).to(config.DEVICE)
+# iterate over the randomly selected test image paths
+for path in imagePaths:
+	# make predictions and visualize the results
+	make_predictions(unet, path)
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diff --git a/test1.py b/test1.py
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+++ b/test1.py
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+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from torchvision import transforms
+import cv2
+from PIL import Image
+from pyimagesearch import config
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+# Define the U-Net model architecture (similar to what's in the article)
+# Ensure to load the model and its weights as per your training script
+
+# Function to preprocess the input image
+def preprocess_image(image_path):
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # Define your preprocessing steps (resize, normalize, etc.)
+    transform = transforms.Compose([transforms.ToPILImage(),
+ 	    transforms.Resize((config.INPUT_IMAGE_HEIGHT,
+		config.INPUT_IMAGE_WIDTH)),
+	    transforms.ToTensor()])
+    image = transform(image)
+    image = image.unsqueeze(0)  # Add batch dimension
+    return image
+
+# Function to predict the mask using the trained U-Net model
+def predict_mask(model, image):
+    model.eval()
+    with torch.no_grad():
+        output = model(image)
+    predicted_mask = torch.argmax(output, dim=1).squeeze(0)
+    print(predicted_mask)
+    return predicted_mask.numpy()
+
+# Function to display the original image and its predicted mask
+def display_image_with_mask(image_path, mask):
+    image = Image.open(image_path)
+    plt.figure(figsize=(10, 5))
+    plt.subplot(1, 2, 1)
+    plt.imshow(image)
+    plt.title('Original Image')
+    plt.axis('off')
+
+    plt.subplot(1, 2, 2)
+    print(mask.shape)
+    plt.imshow(mask)
+    plt.title('Predicted Mask')
+    plt.axis('off')
+
+    plt.show()
+
+# Main function to run the script
+def main():
+    # Load your trained model
+    model = torch.load(config.MODEL_PATH).to(config.DEVICE)
+
+    # Accept user input for image path
+    image_path = input("Enter path to the image: ")
+
+    # Preprocess the input image
+    image = preprocess_image(image_path)
+    # predicted_mask = Image.open("c:\\Users\\anon\\Desktop\\Summer Research-COPY\\unet\\dataset\\train\\masks\\0aab0afa9c.png").convert("RGB")
+    # Predict the mask
+    predicted_mask = predict_mask(model, image)
+
+    # Display the image with its predicted mask
+    display_image_with_mask(image_path, predicted_mask)
+
+main()
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diff --git a/train.py b/train.py
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+++ b/train.py
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+# USAGE
+# python train.py
+
+# import the necessary packages
+from pyimagesearch.dataset import SegmentationDataset
+from pyimagesearch.model import UNet
+from pyimagesearch import config
+from torch.nn import BCEWithLogitsLoss
+from torch.optim import Adam
+from torch.utils.data import DataLoader
+from sklearn.model_selection import train_test_split
+from torchvision import transforms
+from imutils import paths
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+import torch
+import time
+import os
+
+# load the image and mask filepaths in a sorted manner
+imagePaths = sorted(list(paths.list_images(config.IMAGE_DATASET_PATH)))
+maskPaths = sorted(list(paths.list_images(config.MASK_DATASET_PATH)))
+
+# partition the data into training and testing splits using 85% of
+# the data for training and the remaining 15% for testing
+split = train_test_split(imagePaths, maskPaths, test_size=config.TEST_SPLIT, random_state=42)
+
+# unpack the data split
+(trainImages, testImages) = split[:2]
+(trainMasks, testMasks) = split[2:]
+
+# write the testing image paths to disk so that we can use then
+# when evaluating/testing our model
+print("[INFO] saving testing image paths...")
+f = open(config.TEST_PATHS, "w")
+f.write("\n".join(testImages))
+f.close()
+
+# define transformations
+transforms = transforms.Compose([transforms.ToPILImage(),
+ 	transforms.Resize((config.INPUT_IMAGE_HEIGHT,
+		config.INPUT_IMAGE_WIDTH)),
+	transforms.ToTensor()])
+
+# create the train and test datasets
+trainDS = SegmentationDataset(imagePaths=trainImages, maskPaths=trainMasks,
+	transforms=transforms)
+testDS = SegmentationDataset(imagePaths=testImages, maskPaths=testMasks,
+    transforms=transforms)
+print(f"[INFO] found {len(trainDS)} examples in the training set...")
+print(f"[INFO] found {len(testDS)} examples in the test set...")
+
+# create the training and test data loaders
+trainLoader = DataLoader(trainDS, shuffle=True,
+	batch_size=config.BATCH_SIZE, pin_memory=config.PIN_MEMORY,
+	num_workers=0)
+testLoader = DataLoader(testDS, shuffle=False,
+	batch_size=config.BATCH_SIZE, pin_memory=config.PIN_MEMORY,
+	num_workers=0)
+
+# initialize our UNet model
+unet = UNet().to(config.DEVICE)
+
+# initialize loss function and optimizer
+lossFunc = BCEWithLogitsLoss()
+opt = Adam(unet.parameters(), lr=config.INIT_LR)
+
+# calculate steps per epoch for training and test set
+trainSteps = len(trainDS) // config.BATCH_SIZE
+testSteps = len(testDS) // config.BATCH_SIZE
+
+# initialize a dictionary to store training history
+H = {"train_loss": [], "test_loss": []}
+
+# loop over epochs
+print("[INFO] training the network...")
+startTime = time.time()
+for e in tqdm(range(config.NUM_EPOCHS)):
+	# set the model in training mode
+	unet.train()
+	
+	# initialize the total training and validation loss
+	totalTrainLoss = 0
+	totalTestLoss = 0
+	
+	# loop over the training set
+	for (i, (x, y)) in enumerate(trainLoader):
+		# send the input to the device
+		(x, y) = (x.to(config.DEVICE), y.to(config.DEVICE))
+		
+		# perform a forward pass and calculate the training loss
+		pred = unet(x)
+		loss = lossFunc(pred, y)
+		
+		# first, zero out any previously accumulated gradients, then
+		# perform backpropagation, and then update model parameters
+		opt.zero_grad()
+		loss.backward()
+		opt.step()
+		
+		# add the loss to the total training loss so far
+		totalTrainLoss += loss
+		
+	# switch off autograd
+	with torch.no_grad():
+		# set the model in evaluation mode
+		unet.eval()
+		
+		# loop over the validation set
+		for (x, y) in testLoader:
+			# send the input to the device
+			(x, y) = (x.to(config.DEVICE), y.to(config.DEVICE))
+			
+			# make the predictions and calculate the validation loss
+			pred = unet(x)
+			totalTestLoss += lossFunc(pred, y)
+			
+	# calculate the average training and validation loss
+	avgTrainLoss = totalTrainLoss / trainSteps
+	avgTestLoss = totalTestLoss / testSteps
+	
+	# update our training history
+	H["train_loss"].append(avgTrainLoss.cpu().detach().numpy())
+	H["test_loss"].append(avgTestLoss.cpu().detach().numpy())
+	
+	# print the model training and validation information
+	print("[INFO] EPOCH: {}/{}".format(e + 1, config.NUM_EPOCHS))
+	print("Train loss: {:.6f}, Test loss: {:.4f}".format(
+		avgTrainLoss, avgTestLoss))
+	
+# display the total time needed to perform the training
+endTime = time.time()
+print("[INFO] total time taken to train the model: {:.2f}s".format(
+	endTime - startTime))
+
+# plot the training loss
+plt.style.use("ggplot")
+plt.figure()
+plt.plot(H["train_loss"], label="train_loss")
+plt.plot(H["test_loss"], label="test_loss")
+plt.title("Training Loss on Dataset")
+plt.xlabel("Epoch #")
+plt.ylabel("Loss")
+plt.legend(loc="lower left")
+plt.savefig(config.PLOT_PATH)
+
+# serialize the model to disk
+torch.save(unet, config.MODEL_PATH)
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