--- a
+++ b/Classifier/Classes/Data.py
@@ -0,0 +1,78 @@
+#############################################################################################
+#
+# Project:       Mascot Defense Research Project
+# Repository:    ALL Detection System 2020
+# Project:       AllDS2020 Classifier
+#
+# Author:        Allen Akhaumere
+# Contributors:
+# Title:         Data Class
+# Description:   Data class for the Acute Lymphoblastic Leukemia Pytorch CNN Classifier
+#                ALL Classifier.
+# License:       MIT License
+# Last Modified: 2020-07-23
+#
+############################################################################################
+
+import os
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms, datasets
+
+
+
+
+class LeukemiaDataset(Dataset):
+
+    """
+    Acute Lymphoblastic Leukemia Dataset Reader.
+
+     Args:
+           df_data: Dataframe for CSV file
+           data_dir: path to Lymphoblastic Leukemia Data
+           transform: transforms for performing data augmentation
+    """
+
+    def __init__(self, df_data, data_dir='./', transform=None):
+        super().__init__()
+        self.df = df_data.values
+        self.data_dir = data_dir
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, index):
+        img_name, label = self.df[index]
+        img_path = os.path.join(self.data_dir, img_name + '.jpg')
+        image = Image.open(img_path)
+        if self.transform is not None:
+            image = self.transform(image)
+        return image, label
+
+
+def augmentation():
+    """Acute Lymphoblastic Leukemia data augmentation"""
+    mean, std_dev = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
+    training_transforms = transforms.Compose([transforms.Resize((100, 100)),
+                                              transforms.RandomRotation(30),
+                                              transforms.RandomResizedCrop(100),
+                                              transforms.RandomHorizontalFlip(),
+                                              transforms.RandomGrayscale(p=0.1),
+                                              transforms.ToTensor(),
+                                              transforms.Normalize(mean=mean, std=std_dev)])
+
+    validation_transforms = transforms.Compose([
+        transforms.Resize((100, 100)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=mean, std=std_dev)])
+
+    return training_transforms, validation_transforms
+
+
+# Load the datasets with ImageFolder
+def load_datasets(train_dir, training_transforms, valid_dir, validation_transforms):
+    """ """
+    training_dataset = datasets.ImageFolder(train_dir, transform=training_transforms)
+    validation_dataset = datasets.ImageFolder(valid_dir, transform=validation_transforms)
+    return training_dataset, validation_dataset
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