diff --git a/pyimagesearch/config.py b/pyimagesearch/config.py
new file mode 100644
index 0000000..63b49e5
--- /dev/null
+++ b/pyimagesearch/config.py
@@ -0,0 +1,47 @@
+# import the necessary packages
+import torch
+import os
+
+# base path of the dataset
+DATASET_PATH = os.path.join("dataset", "train")
+
+# define the path to the images and masks dataset
+IMAGE_DATASET_PATH = os.path.join(DATASET_PATH, "images")
+MASK_DATASET_PATH = os.path.join(DATASET_PATH, "masks")
+
+# define the test split
+TEST_SPLIT = 0.15
+
+# determine the device to be used for training and evaluation
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# determine if we will be pinning memory during data loading
+PIN_MEMORY = True if DEVICE == "cuda" else False
+
+# define the number of channels in the input, number of classes,
+# and number of levels in the U-Net model
+NUM_CHANNELS = 1
+NUM_CLASSES = 1
+NUM_LEVELS = 3
+
+# initialize learning rate, number of epochs to train for, and the
+# batch size
+INIT_LR = 0.001
+NUM_EPOCHS = 40
+BATCH_SIZE = 64
+
+# define the input image dimensions
+INPUT_IMAGE_WIDTH = 128
+INPUT_IMAGE_HEIGHT = 128
+
+# define threshold to filter weak predictions
+THRESHOLD = 0.5
+
+# define the path to the base output directory
+BASE_OUTPUT = "output"
+
+# define the path to the output serialized model, model training
+# plot, and testing image paths
+MODEL_PATH = os.path.join(BASE_OUTPUT, "unet_tgs_salt.pth")
+PLOT_PATH = os.path.sep.join([BASE_OUTPUT, "plot.png"])
+TEST_PATHS = os.path.sep.join([BASE_OUTPUT, "test_paths.txt"])
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diff --git a/pyimagesearch/dataset.py b/pyimagesearch/dataset.py
new file mode 100644
index 0000000..3ffa276
--- /dev/null
+++ b/pyimagesearch/dataset.py
@@ -0,0 +1,34 @@
+# import the necessary packages
+from torch.utils.data import Dataset
+import cv2
+
+class SegmentationDataset(Dataset):
+	def __init__(self, imagePaths, maskPaths, transforms):
+		# store the image and mask filepaths, and augmentation
+		# transforms
+		self.imagePaths = imagePaths
+		self.maskPaths = maskPaths
+		self.transforms = transforms
+		
+	def __len__(self):
+		# return the number of total samples contained in the dataset
+		return len(self.imagePaths)
+	
+	def __getitem__(self, idx):
+		# grab the image path from the current index
+		imagePath = self.imagePaths[idx]
+		
+		# load the image from disk, swap its channels from BGR to RGB,
+		# and read the associated mask from disk in grayscale mode
+		image = cv2.imread(imagePath)
+		image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+		mask = cv2.imread(self.maskPaths[idx], 0)
+		
+		# check to see if we are applying any transformations
+		if self.transforms is not None:
+			# apply the transformations to both image and its mask
+			image = self.transforms(image)
+			mask = self.transforms(mask)
+			
+		# return a tuple of the image and its mask
+		return (image, mask)
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diff --git a/pyimagesearch/model.py b/pyimagesearch/model.py
new file mode 100644
index 0000000..496d014
--- /dev/null
+++ b/pyimagesearch/model.py
@@ -0,0 +1,124 @@
+# import the necessary packages
+from . import config
+from torch.nn import ConvTranspose2d
+from torch.nn import Conv2d
+from torch.nn import MaxPool2d
+from torch.nn import Module
+from torch.nn import ModuleList
+from torch.nn import ReLU
+from torchvision.transforms import CenterCrop
+from torch.nn import functional as F
+import torch
+
+class Block(Module):
+	def __init__(self, inChannels, outChannels):
+		super().__init__()
+		# store the convolution and RELU layers
+		self.conv1 = Conv2d(inChannels, outChannels, 3)
+		self.relu = ReLU()
+		self.conv2 = Conv2d(outChannels, outChannels, 3)
+		
+	def forward(self, x):
+		# apply CONV => RELU => CONV block to the inputs and return it
+		return self.conv2(self.relu(self.conv1(x)))
+	
+class Encoder(Module):
+	def __init__(self, channels=(3, 16, 32, 64)):
+		super().__init__()
+		# store the encoder blocks and maxpooling layer
+		self.encBlocks = ModuleList(
+			[Block(channels[i], channels[i + 1])
+			 	for i in range(len(channels) - 1)])
+		self.pool = MaxPool2d(2)
+		
+	def forward(self, x):
+		# initialize an empty list to store the intermediate outputs
+		blockOutputs = []
+		
+		# loop through the encoder blocks
+		for block in self.encBlocks:
+			# pass the inputs through the current encoder block, store
+			# the outputs, and then apply maxpooling on the output
+			x = block(x)
+			blockOutputs.append(x)
+			x = self.pool(x)
+			
+		# return the list containing the intermediate outputs
+		return blockOutputs
+	
+
+class Decoder(Module):
+	def __init__(self, channels=(64, 32, 16)):
+		super().__init__()
+		# initialize the number of channels, upsampler blocks, and
+		# decoder blocks
+		self.channels = channels
+		self.upconvs = ModuleList(
+			[ConvTranspose2d(channels[i], channels[i + 1], 2, 2)
+			 	for i in range(len(channels) - 1)])
+		self.dec_blocks = ModuleList(
+			[Block(channels[i], channels[i + 1])
+			 	for i in range(len(channels) - 1)])
+		
+	def forward(self, x, encFeatures):
+		# loop through the number of channels
+		for i in range(len(self.channels) - 1):
+			# pass the inputs through the upsampler blocks
+			x = self.upconvs[i](x)
+			
+			# crop the current features from the encoder blocks,
+			# concatenate them with the current upsampled features,
+			# and pass the concatenated output through the current
+			# decoder block
+			encFeat = self.crop(encFeatures[i], x)
+			x = torch.cat([x, encFeat], dim=1)
+			x = self.dec_blocks[i](x)
+			
+		# return the final decoder output
+		return x
+	
+	def crop(self, encFeatures, x):
+		# grab the dimensions of the inputs, and crop the encoder
+		# features to match the dimensions
+		(_, _, H, W) = x.shape
+		encFeatures = CenterCrop([H, W])(encFeatures)
+		
+		# return the cropped features
+		return encFeatures
+	
+    
+class UNet(Module):
+	def __init__(self, encChannels=(3, 16, 32, 64),
+		 decChannels=(64, 32, 16),
+		 nbClasses=1, retainDim=True,
+		 outSize=(config.INPUT_IMAGE_HEIGHT,  config.INPUT_IMAGE_WIDTH)):
+		super().__init__()
+		# initialize the encoder and decoder
+		self.encoder = Encoder(encChannels)
+		self.decoder = Decoder(decChannels)
+		
+		# initialize the regression head and store the class variables
+		self.head = Conv2d(decChannels[-1], nbClasses, 1)
+		self.retainDim = retainDim
+		self.outSize = outSize
+		
+	def forward(self, x):
+			# grab the features from the encoder
+			encFeatures = self.encoder(x)
+			
+			# pass the encoder features through decoder making sure that
+			# their dimensions are suited for concatenation
+			decFeatures = self.decoder(encFeatures[::-1][0],
+				encFeatures[::-1][1:])
+			
+			# pass the decoder features through the regression head to
+			# obtain the segmentation mask
+			map = self.head(decFeatures)
+			
+			# check to see if we are retaining the original output
+			# dimensions and if so, then resize the output to match them
+			if self.retainDim:
+				map = F.interpolate(map, self.outSize)
+				
+			# return the segmentation map
+			return map
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