diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py
index aa1c46d..32a715e 100644
--- a/face_swapper/src/dataset.py
+++ b/face_swapper/src/dataset.py
@@ -2,6 +2,8 @@
 import os
 import random
 
+import albumentations
+import torch
 from torch import Tensor
 from torch.utils.data import Dataset
 from torchvision import io, transforms
@@ -16,6 +18,7 @@ def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_mode
 		self.warp_template = warp_template
 		self.batch_mode = batch_mode
 		self.batch_ratio = batch_ratio
+		self.augmentations = self.compose_augmentations()
 		self.transforms = self.compose_transforms()
 
 	def __getitem__(self, index : int) -> Batch:
@@ -32,31 +35,48 @@ def __getitem__(self, index : int) -> Batch:
 	def __len__(self) -> int:
 		return len(self.file_paths)
 
+	def compose_augmentations(self) -> albumentations.Compose:
+		return albumentations.Compose(
+		[
+			albumentations.RandomBrightnessContrast(p = 0.3),
+			albumentations.OneOf([
+				albumentations.MotionBlur(p = 0.1),
+				albumentations.MedianBlur(p = 0.1)
+			], p = 0.3),
+			albumentations.ColorJitter(p = 0.1),
+			albumentations.ToTensorV2()
+		])
+
 	def compose_transforms(self) -> transforms:
 		return transforms.Compose(
 		[
-			transforms.ToPILImage(),
-			transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC),
-			transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1),
-			transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)),
-			transforms.ToTensor(),
+			transforms.Resize(256),
 			transforms.Lambda(self.warp_tensor),
 			transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
 		])
 
+	def apply_augmentations(self, input_tensor : Tensor) -> Tensor:
+		input_frame = input_tensor.numpy().transpose(1, 2, 0)
+		output_tensor = self.augmentations(image = input_frame)['image']
+		output_tensor = output_tensor.to(torch.float32) / 255
+		return output_tensor
+
 	def warp_tensor(self, temp_tensor : Tensor) -> Tensor:
 		return warp_tensor(temp_tensor.unsqueeze(0), self.warp_template).squeeze(0)
 
 	def prepare_different_batch(self, source_path : str) -> Batch:
 		target_path = random.choice(self.file_paths)
 		source_tensor = io.read_image(source_path)
+		source_tensor = self.apply_augmentations(source_tensor)
 		source_tensor = self.transforms(source_tensor)
 		target_tensor = io.read_image(target_path)
+		target_tensor = self.apply_augmentations(target_tensor)
 		target_tensor = self.transforms(target_tensor)
 		return source_tensor, target_tensor
 
 	def prepare_equal_batch(self, source_path : str) -> Batch:
 		source_tensor = io.read_image(source_path)
+		source_tensor = self.apply_augmentations(source_tensor)
 		source_tensor = self.transforms(source_tensor)
 		return source_tensor, source_tensor
 
@@ -65,7 +85,9 @@ def prepare_same_batch(self, source_path : str) -> Batch:
 		target_file_name_and_extension = random.choice(os.listdir(target_directory_path))
 		target_path = os.path.join(target_directory_path, target_file_name_and_extension)
 		source_tensor = io.read_image(source_path)
+		source_tensor = self.apply_augmentations(source_tensor)
 		source_tensor = self.transforms(source_tensor)
 		target_tensor = io.read_image(target_path)
+		target_tensor = self.apply_augmentations(target_tensor)
 		target_tensor = self.transforms(target_tensor)
 		return source_tensor, target_tensor