diff --git a/labs/06/svhn_competition.py b/labs/06/svhn_competition.py
index 7e95de3..80136f4 100644
--- a/labs/06/svhn_competition.py
+++ b/labs/06/svhn_competition.py
@@ -3,10 +3,12 @@
 import datetime
 import os
 import re
+
 os.environ.setdefault("KERAS_BACKEND", "torch")  # Use PyTorch backend unless specified otherwise
 
 import keras
 import numpy as np
+from torchvision.transforms import Resize
 import torch
 
 import bboxes_utils
@@ -66,17 +68,58 @@ def main(args: argparse.Namespace) -> None:
     # - "classes", a `[num_digits]` vector with classes of image digits,
     # - "bboxes", a `[num_digits, 4]` vector with bounding boxes of image digits.
     svhn = SVHN()
-    def get_inputs(dataset=svhn.train):
-        imgs, clsses, bboxes = [], [], []
-        for example in dataset:
-            img, cls, bbox = example["image"], example["classes"], example["bboxes"] 
-            imgs.append(img)
-            clsses.append(cls)
-            bboxes.append(bbox)
-        return np.array(imgs), np.array(clsses), np.array(bboxes)
-    train_imgs, train_clsses, train_bboxes = get_inputs()
-    dev_imgs, dev_clsses, dev_bboxes = get_inputs(svhn.dev)
-    test_imgs, _, _ = get_inputs(svhn.test)
+
+    def transform_data(example):
+        image = example["image"]
+        classes = example["classes"]  # Gold classes
+        bboxes = example["bboxes"]  # Gold bboxes
+
+        threshhold = 0.5
+
+        # Resize image
+        resize_transform = Resize((224, 224))
+        resized_image = resize_transform(image)
+
+        anchors = []
+
+        anchor_size = 2 ** (2 / 3) * 4 * (2**7)
+
+        for i in range(7):
+            for j in range(7):
+                # top, left, bottom, right
+                anchors.append(
+                    [
+                        i * anchor_size,
+                        j * anchor_size,
+                        (i + 1) * anchor_size,
+                        (j + 1) * anchor_size,
+                    ]
+                )
+
+        anchors = np.array(anchors)
+        anchor_classes, anchor_bboxes = bboxes_utils.bboxes_training(
+            anchors, bboxes, classes, threshhold
+        )
+        onehot_encoded_anchor_classes = keras.ops.one_hot(anchor_classes, svhn.LABELS)
+
+        # If class is zero, return 1, otherwise return 0
+        is_background_weights = map(lambda x: x / svhn.LABELS, anchor_classes)
+
+        return (
+            resized_image,
+            (onehot_encoded_anchor_classes, anchor_bboxes),
+            is_background_weights,
+        )
+
+    transformed_train_dataset = torch.utils.data.DataLoader(
+        svhn.train.transform(transform_data), batch_size=args.batch_size, shuffle=True
+    )
+    transformed_dev_dataset = torch.utils.data.DataLoader(
+        svhn.dev.transform(transform_data), batch_size=args.batch_size, shuffle=False
+    )
+    transformed_test_dataset = torch.utils.data.DataLoader(
+        svhn.test.transform(transform_data), batch_size=args.batch_size, shuffle=False
+    )
 
     # Load the EfficientNetV2-B0 model. It assumes the input images are
     # represented in the [0-255] range.
@@ -92,15 +135,15 @@ def get_inputs(dataset=svhn.train):
     )
 
     # TODO: Create the model and train it
-    backbone.trainable =  False
-    inputs = keras.layers.Input(shape=train_imgs.shape)
+    backbone.trainable = False
+    inputs = keras.layers.Input(shape=(224,224,3))
     # backbone outputs bottom to up: block1a, block2b, block3b, block5e, top
     # shapes: 7x7x1280, 14x14x112, 28x28x40, 56x56x24, 112x112x16
     # top, block5e, block3b, block2b, block1a = backbone(inputs)
 
     # FPN: feature pyramid network
     # 1. First build feature pyramid to extract features, using 3 layers (layer 3-5)
-    def fpn(backbone=backbone, inputs=inputs, layers=(3,4,5)):
+    def fpn(backbone=backbone, inputs=inputs, layers=(3, 4, 5)):
         conv3_11 = keras.layers.Conv2D(256, 1, 1, "same")
         conv4_11 = keras.layers.Conv2D(256, 1, 1, "same")
         conv5_11 = keras.layers.Conv2D(256, 1, 1, "same")
@@ -123,6 +166,7 @@ def fpn(backbone=backbone, inputs=inputs, layers=(3,4,5)):
         p6_output = conv6_33(c5_output)
         p7_output = conv7_33(keras.activations.relu(p6_output))
         return p3_output, p4_output, p5_output, p6_output, p7_output
+
     ### classification and bbox regression head
     ### 9 is the anchor number
     def heads(input_feature, type="classification"):
@@ -139,7 +183,7 @@ def heads(input_feature, type="classification"):
         conv4 = keras.layers.ReLU()(keras.layers.Conv2D(256, 3, 1, "same")(conv3))
         outputs = keras.layers.Conv2D(output_size, 3, 1, "same", activation=activ)(conv4)
         return outputs
-    
+
     fpn_features = fpn()
     cls_outputs, reg_outputs = [], []
     for feature in fpn_features:
@@ -147,7 +191,7 @@ def heads(input_feature, type="classification"):
         reg_output = heads(feature, "regression")
         cls_outputs.append(keras.ops.reshape(cls_output, (args.batch_size, -1, svhn.LABELS)))
         reg_outputs.append(keras.ops.reshape(reg_output, (args.batch_size, -1, 4)))
-        
+
     cls_outputs = keras.ops.concatenate(cls_outputs, axis=1)
     reg_outputs = keras.ops.concatenate(reg_outputs, axis=1)
     model_outputs = keras.ops.concatenate([reg_outputs, cls_outputs], axis=-1)
@@ -156,12 +200,15 @@ def heads(input_feature, type="classification"):
 
     model.compile(
         optimizer=keras.optimizers.Adam(learning_rate=args.learning_rate),
-        loss=(
-            keras.losses.BinaryFocalCrossEntropy(),
-            keras.losses.Huber()),
+        loss=(keras.losses.BinaryFocalCrossentropy(), keras.losses.Huber()),
     )
 
-    model.fit()
+    model.fit(
+        transformed_train_dataset,
+        epochs=args.epochs,
+        validation_data=transformed_dev_dataset,
+        callbacks=[TorchTensorBoardCallback(args.logdir)],
+    )
 
     # Generate test set annotations, but in `args.logdir` to allow parallel execution.
     os.makedirs(args.logdir, exist_ok=True)