diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/environment.yml b/environment.yml
index c003c73..8e5f477 100644
--- a/environment.yml
+++ b/environment.yml
@@ -9,7 +9,8 @@ dependencies:
   - sphinx-autobuild
   - sphinx-rtd-theme
   - pip
-  - h5py
+  - h5py==3.12.1
+  - hdf5==1.14.4
   - black
   - isort
   - jupyterlab
@@ -20,6 +21,8 @@ dependencies:
   - scalene
   - tqdm
   - scipy
+  - wandb
+  - scikit-learn
   - pip:
     - torch
     - torchvision
diff --git a/main.py b/main.py
index d9f233f..65ecc86 100644
--- a/main.py
+++ b/main.py
@@ -7,6 +7,7 @@
 
 import wandb
 from utils import MetricWrapper, createfolders, get_args, load_data, load_model
+from wandb_api import WANDB_API
 
 
 def main():
@@ -29,33 +30,38 @@ def main():
 
     device = args.device
 
-    if args.dataset.lower() in ["usps_0-6", "uspsh5_7_9"]:
-        augmentations = transforms.Compose(
+    if args.dataset.lower() in ["usps_0-6", "usps_7-9"]:
+        transform = transforms.Compose(
             [
                 transforms.Resize((16, 16)),
                 transforms.ToTensor(),
             ]
         )
     else:
-        augmentations = transforms.Compose([transforms.ToTensor()])
+        transform = transforms.Compose([transforms.ToTensor()])
 
-    # Dataset
-    traindata = load_data(
+    traindata, validata, testdata = load_data(
         args.dataset,
-        train=True,
-        data_path=args.datafolder,
-        download=args.download_data,
-        transform=augmentations,
-    )
-    validata = load_data(
-        args.dataset,
-        train=False,
-        data_path=args.datafolder,
-        download=args.download_data,
-        transform=augmentations,
+        data_dir=args.datafolder,
+        transform=transform,
+        val_size=args.val_size,
     )
 
-    metrics = MetricWrapper(*args.metric, num_classes=traindata.num_classes)
+    train_metrics = MetricWrapper(
+        *args.metric,
+        num_classes=traindata.num_classes,
+        macro_averaging=args.macro_averaging,
+    )
+    val_metrics = MetricWrapper(
+        *args.metric,
+        num_classes=traindata.num_classes,
+        macro_averaging=args.macro_averaging,
+    )
+    test_metrics = MetricWrapper(
+        *args.metric,
+        num_classes=traindata.num_classes,
+        macro_averaging=args.macro_averaging,
+    )
 
     # Find the shape of the data, if is 2D, add a channel dimension
     data_shape = traindata[0][0].shape
@@ -80,6 +86,9 @@ def main():
     valiloader = DataLoader(
         validata, batch_size=args.batchsize, shuffle=False, pin_memory=True
     )
+    testloader = DataLoader(
+        testdata, batch_size=args.batchsize, shuffle=False, pin_memory=True
+    )
 
     criterion = nn.CrossEntropyLoss()
     optimizer = th.optim.Adam(model.parameters(), lr=args.learning_rate)
@@ -104,22 +113,22 @@ def main():
             optimizer.step()
             optimizer.zero_grad(set_to_none=True)
 
-            metrics(y, logits)
+            train_metrics(y, logits)
 
             break
-        print(metrics.accumulate())
+        print(train_metrics.accumulate())
         print("Dry run completed successfully.")
         exit()
 
     # wandb.login(key=WANDB_API)
     wandb.init(
-            entity="ColabCode-org",
-            # entity="FYS-8805 Exam",
-            project="Test", 
-            tags=[args.modelname, args.dataset]
-            )
+        entity="ColabCode",
+        # entity="FYS-8805 Exam",
+        project="Jan",
+        tags=[args.modelname, args.dataset],
+    )
     wandb.watch(model)
-    exit()
+
     for epoch in range(args.epoch):
         # Training loop start
         trainingloss = []
@@ -135,33 +144,49 @@ def main():
             optimizer.zero_grad(set_to_none=True)
             trainingloss.append(loss.item())
 
-            metrics(y, logits)
-
-        wandb.log(metrics.accumulate(str_prefix="Train "))
-        metrics.reset()
+            train_metrics(y, logits)
 
-        evalloss = []
-        # Eval loop start
+        valloss = []
+        # Validation loop start
         model.eval()
         with th.no_grad():
             for x, y in tqdm(valiloader, desc="Validation"):
                 x, y = x.to(device), y.to(device)
                 logits = model.forward(x)
                 loss = criterion(logits, y)
-                evalloss.append(loss.item())
-
-                metrics(y, logits)
+                valloss.append(loss.item())
 
-        wandb.log(metrics.accumulate(str_prefix="Evaluation "))
-        metrics.reset()
+                val_metrics(y, logits)
 
         wandb.log(
             {
                 "Epoch": epoch,
                 "Train loss": np.mean(trainingloss),
-                "Evaluation Loss": np.mean(evalloss),
+                "Validation loss": np.mean(valloss),
             }
+            | train_metrics.accumulate(str_prefix="Train ")
+            | val_metrics.accumulate(str_prefix="Validation ")
         )
+        train_metrics.reset()
+        val_metrics.reset()
+
+    testloss = []
+    model.eval()
+    with th.no_grad():
+        for x, y in tqdm(testloader, desc="Testing"):
+            x, y = x.to(device), y.to(device)
+            logits = model.forward(x)
+            loss = criterion(logits, y)
+            testloss.append(loss.item())
+
+            preds = th.argmax(logits, dim=1)
+            test_metrics(y, preds)
+
+    wandb.log(
+        {"Epoch": 1, "Test loss": np.mean(testloss)}
+        | test_metrics.accumulate(str_prefix="Test ")
+    )
+    test_metrics.reset()
 
 
 if __name__ == "__main__":
diff --git a/pyproject.toml b/pyproject.toml
index e1d02b6..93ccc07 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,29 @@
+[project]
+name = "collaborative-coding-exam"
+version = "0.1.0"
+description = "Exam project in the collaborative coding course."
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "black>=25.1.0",
+    "h5py>=3.12.1",
+    "isort>=6.0.0",
+    "jupyterlab>=4.3.5",
+    "numpy>=2.2.2",
+    "pandas>=2.2.3",
+    "pip>=25.0",
+    "pytest>=8.3.4",
+    "ruff>=0.9.4",
+    "scalene>=1.5.51",
+    "scikit-learn>=1.6.1",
+    "sphinx>=8.1.3",
+    "sphinx-autoapi>=3.4.0",
+    "sphinx-autobuild>=2024.10.3",
+    "sphinx-rtd-theme>=3.0.2",
+    "torch>=2.6.0",
+    "torchvision>=0.21.0",
+    "tqdm>=4.67.1",
+]
 [tool.isort]
 profile = "black"
 line_length = 88
diff --git a/tests/test_dataloaders.py b/tests/test_dataloaders.py
index 9f58ae4..32634d6 100644
--- a/tests/test_dataloaders.py
+++ b/tests/test_dataloaders.py
@@ -17,18 +17,25 @@ def test_uspsdataset0_6():
 
         # Create a h5 file
         with h5py.File(tf, "w") as f:
+            targets = np.array([6, 5, 4, 3, 2, 1, 0, 0, 0, 0])
+            indices = np.arange(len(targets))
             # Populate the file with data
             f["train/data"] = np.random.rand(10, 16 * 16)
-            f["train/target"] = np.array([6, 5, 4, 3, 2, 1, 0, 0, 0, 0])
+            f["train/target"] = targets
 
         trans = transforms.Compose(
             [
-                transforms.Resize((16, 16)),  # At least for USPS
+                transforms.Resize((16, 16)),
                 transforms.ToTensor(),
             ]
         )
-        dataset = USPSDataset0_6(data_path=tempdir, train=True, transform=trans)
+        dataset = USPSDataset0_6(
+            data_path=tempdir,
+            sample_ids=indices,
+            train=True,
+            transform=trans,
+        )
         assert len(dataset) == 10
         data, target = dataset[0]
         assert data.shape == (1, 16, 16)
-        assert all(target == np.array([0, 0, 0, 0, 0, 0, 1]))
+        assert target == 6
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index d6da0ab..eca71fa 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1,19 +1,65 @@
+from random import randint
+
+import pytest
+
+from utils.load_metric import MetricWrapper
 from utils.metrics import Accuracy, F1Score, Precision, Recall
 
 
-def test_recall():
+@pytest.mark.parametrize(
+    "metric, num_classes, macro_averaging",
+    [
+        ("f1", randint(2, 10), False),
+        ("f1", randint(2, 10), True),
+        ("recall", randint(2, 10), False),
+        ("recall", randint(2, 10), True),
+        ("accuracy", randint(2, 10), False),
+        ("accuracy", randint(2, 10), True),
+        ("precision", randint(2, 10), False),
+        ("precision", randint(2, 10), True),
+        # TODO: Add test for EntropyPrediction
+    ],
+)
+def test_metric_wrapper(metric, num_classes, macro_averaging):
+    import numpy as np
     import torch
 
-    recall = Recall(7)
+    y_true = torch.arange(num_classes, dtype=torch.int64)
+    logits = torch.rand(num_classes, num_classes)
+
+    metrics = MetricWrapper(
+        metric,
+        num_classes=num_classes,
+        macro_averaging=macro_averaging,
+    )
+
+    metrics(y_true, logits)
+    score = metrics.accumulate()
+    metrics.reset()
+    empty_score = metrics.accumulate()
+
+    assert isinstance(score, dict), "Expected a dictionary output."
+    assert metric in score, f"Expected {metric} metric in the output."
+    assert score[metric] >= 0, "Expected a non-negative value."
+    assert np.isnan(empty_score[metric]), "Expected an empty list."
+
+
+def test_recall():
+    import torch
 
     y_true = torch.tensor([0, 1, 2, 3, 4, 5, 6])
-    y_pred = torch.tensor([2, 1, 2, 1, 4, 5, 6])
+    logits = torch.randn(7, 7)
 
-    recall_score = recall(y_true, y_pred)
+    recall_micro = Recall(7)
+    recall_macro = Recall(7, macro_averaging=True)
 
-    assert recall_score.allclose(torch.tensor(0.7143), atol=1e-5), (
-        f"Recall Score: {recall_score.item()}"
-    )
+    recall_micro_score = recall_micro(y_true, logits)
+    recall_macro_score = recall_macro(y_true, logits)
+
+    assert isinstance(recall_micro_score, torch.Tensor), "Expected a tensor output."
+    assert isinstance(recall_macro_score, torch.Tensor), "Expected a tensor output."
+    assert recall_micro_score.item() >= 0, "Expected a non-negative value."
+    assert recall_macro_score.item() >= 0, "Expected a non-negative value."
 
 
 def test_f1score():
@@ -26,62 +72,59 @@ def test_f1score():
 
     target = torch.tensor([0, 1, 0, 2])
 
-    f1_metric.update(preds, target)
+    f1_metric(preds, target)
     assert f1_metric.tp.sum().item() > 0, "Expected some true positives."
     assert f1_metric.fp.sum().item() > 0, "Expected some false positives."
     assert f1_metric.fn.sum().item() > 0, "Expected some false negatives."
 
 
-def test_precision_case1():
-    import torch
+def test_precision():
+    from random import randint
 
-    for boolean, true_precision in zip([True, False], [25.0 / 36, 7.0 / 10]):
-        true1 = torch.tensor([0, 1, 2, 1, 0, 2, 1, 0, 2, 1])
-        pred1 = torch.tensor([0, 2, 1, 1, 0, 2, 0, 0, 2, 1])
-        P = Precision(3, use_mean=boolean)
-        precision1 = P(true1, pred1)
-        assert precision1.allclose(torch.tensor(true_precision), atol=1e-5), (
-            f"Precision Score: {precision1.item()}"
-        )
+    import numpy as np
+    import torch
+    from sklearn.metrics import precision_score
 
+    C = randint(2, 10)  # number of classes
+    N = randint(2, 10 * C)  # batchsize
+    y_true = torch.randint(0, C, (N,))
+    logits = torch.randn(N, C)
 
-def test_precision_case2():
-    import torch
+    # create metric objects
+    precision_micro = Precision(num_classes=C)
+    precision_macro = Precision(num_classes=C, macro_averaging=True)
 
-    for boolean, true_precision in zip([True, False], [8.0 / 15, 6.0 / 15]):
-        true2 = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])
-        pred2 = torch.tensor([0, 0, 4, 3, 4, 0, 4, 4, 2, 3, 4, 1, 2, 4, 0])
-        P = Precision(5, use_mean=boolean)
-        precision2 = P(true2, pred2)
-        assert precision2.allclose(torch.tensor(true_precision), atol=1e-5), (
-            f"Precision Score: {precision2.item()}"
-        )
+    # find scores
+    micro_precision_score = precision_micro(y_true, logits)
+    macro_precision_score = precision_macro(y_true, logits)
 
+    # check output to be tensor
+    assert isinstance(micro_precision_score, torch.Tensor), "Tensor output is expected."
+    assert isinstance(macro_precision_score, torch.Tensor), "Tensor output is expected."
 
-def test_precision_case3():
-    import torch
+    # check for non-negativity
+    assert micro_precision_score.item() >= 0, "Expected non-negative value"
+    assert macro_precision_score.item() >= 0, "Expected non-negative value"
 
-    for boolean, true_precision in zip([True, False], [3.0 / 4, 4.0 / 5]):
-        true3 = torch.tensor([0, 0, 0, 1, 0])
-        pred3 = torch.tensor([1, 0, 0, 1, 0])
-        P = Precision(2, use_mean=boolean)
-        precision3 = P(true3, pred3)
-        assert precision3.allclose(torch.tensor(true_precision), atol=1e-5), (
-            f"Precision Score: {precision3.item()}"
-        )
+    # find predictions
+    y_pred = logits.argmax(dim=-1, keepdims=True)
 
+    # check dimension
+    assert y_true.shape == torch.Size([N, 1]) or torch.Size([N])
+    assert logits.shape == torch.Size([N, C])
+    assert y_pred.shape == torch.Size([N, 1]) or torch.Size([N])
 
-def test_for_zero_denominator():
-    import torch
+    # find true values with scikit learn
+    scikit_macro_precision = precision_score(y_true, y_pred, average="macro")
+    scikit_micro_precision = precision_score(y_true, y_pred, average="micro")
 
-    for boolean in [True, False]:
-        true4 = torch.tensor([1, 1, 1, 1, 1])
-        pred4 = torch.tensor([0, 0, 0, 0, 0])
-        P = Precision(2, use_mean=boolean)
-        precision4 = P(true4, pred4)
-        assert precision4.allclose(torch.tensor(0.0), atol=1e-5), (
-            f"Precision Score: {precision4.item()}"
-        )
+    # check for similarity
+    assert np.isclose(scikit_micro_precision, micro_precision_score, atol=1e-5), (
+        "Score does not match scikit's score"
+    )
+    assert np.isclose(scikit_macro_precision, macro_precision_score, atol=1e-5), (
+        "Score does not match scikit's score"
+    )
 
 
 def test_accuracy():
diff --git a/tests/test_models.py b/tests/test_models.py
index efc5412..4dd3fa8 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -32,4 +32,3 @@ def test_jan_model(image_shape, num_classes):
     y = model(x)
 
     assert y.shape == (n, num_classes), f"Shape: {y.shape}"
-
diff --git a/utils/arg_parser.py b/utils/arg_parser.py
index 079eae4..618e8d2 100644
--- a/utils/arg_parser.py
+++ b/utils/arg_parser.py
@@ -33,13 +33,6 @@ def get_args():
         help="Whether model should be saved or not.",
     )
 
-    parser.add_argument(
-        "--download-data",
-        type=bool,
-        default=False,
-        help="Whether the data should be downloaded or not. Might cause code to start a bit slowly.",
-    )
-
     # Data/Model specific values
     parser.add_argument(
         "--modelname",
@@ -61,7 +54,12 @@ def get_args():
         choices=["svhn", "usps_0-6", "usps_7-9", "mnist_0-3", "mnist_4-9"],
         help="Which dataset to train the model on.",
     )
-
+    parser.add_argument(
+        "--val_size",
+        type=float,
+        default=0.2,
+        help="Percentage of training dataset to be used as validation dataset - must be within (0,1).",
+    )
     parser.add_argument(
         "--metric",
         type=str,
@@ -70,20 +68,10 @@ def get_args():
         nargs="+",
         help="Which metric to use for evaluation",
     )
-    
-    parser.add_argument(
-        '--imagesize',
-        type=int,
-        default=28,
-        help='Imagesize'
-    )
-    
     parser.add_argument(
-        '--nr_channels',
-        type=int,
-        default=1,
-        choices=[1,3],
-        help='Number of image channels'
+        "--macro_averaging",
+        action="store_true",
+        help="If the flag is included, the metrics will be calculated using macro averaging.",
     )
 
     # Training specific values
@@ -115,7 +103,7 @@ def get_args():
     parser.add_argument(
         "--dry_run",
         action="store_true",
-        help="If true, the code will not run the training loop.",
+        help="If the flag is included, the code will not run the training loop.",
     )
     args = parser.parse_args()
 
diff --git a/utils/dataloaders/__init__.py b/utils/dataloaders/__init__.py
index a5688ba..192cf7e 100644
--- a/utils/dataloaders/__init__.py
+++ b/utils/dataloaders/__init__.py
@@ -1,6 +1,15 @@
-__all__ = ["USPSDataset0_6", "USPSH5_Digit_7_9_Dataset", "MNISTDataset0_3", "SVHNDataset"]
+__all__ = [
+    "USPSDataset0_6",
+    "USPSH5_Digit_7_9_Dataset",
+    "MNISTDataset0_3",
+    "MNISTDataset4_9",
+    "Downloader",
+    "SVHNDataset",
+]
 
+from .download import Downloader
 from .mnist_0_3 import MNISTDataset0_3
+from .mnist_4_9 import MNISTDataset4_9
+from .svhn import SVHNDataset
 from .usps_0_6 import USPSDataset0_6
 from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset
-from .svhn import SVHNDataset
diff --git a/utils/dataloaders/datasources.py b/utils/dataloaders/datasources.py
index f0d2e01..936d32e 100644
--- a/utils/dataloaders/datasources.py
+++ b/utils/dataloaders/datasources.py
@@ -17,3 +17,26 @@
         "8ea070ee2aca1ac39742fdd1ef5ed118",
     ],
 }
+
+MNIST_SOURCE = {
+    "train_images": [
+        "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz",
+        "train-images-idx3-ubyte",
+        None,
+    ],
+    "train_labels": [
+        "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz",
+        "train-labels-idx1-ubyte",
+        None,
+    ],
+    "test_images": [
+        "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz",
+        "t10k-images-idx3-ubyte",
+        None,
+    ],
+    "test_labels": [
+        "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz",
+        "t10k-labels-idx1-ubyte",
+        None,
+    ],
+}
diff --git a/utils/dataloaders/download.py b/utils/dataloaders/download.py
new file mode 100644
index 0000000..d99eff2
--- /dev/null
+++ b/utils/dataloaders/download.py
@@ -0,0 +1,183 @@
+import bz2
+import gzip
+import hashlib
+import os
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from urllib.request import urlretrieve
+
+import h5py as h5
+import numpy as np
+
+from .datasources import MNIST_SOURCE, USPS_SOURCE
+
+
+class Downloader:
+    """
+    Class to download and load the USPS dataset.
+
+    Methods
+    -------
+    mnist(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
+        Download the MNIST dataset and save it as an HDF5 file to `data_dir`.
+    svhn(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
+        Download the SVHN dataset and save it as an HDF5 file to `data_dir`.
+    usps(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
+        Download the USPS dataset and save it as an HDF5 file to `data_dir`.
+
+    Raises
+    ------
+    NotImplementedError
+        If the download method is not implemented for the dataset.
+
+    Examples
+    --------
+    >>> from pathlib import Path
+    >>> from utils import Downloader
+    >>> dir = Path('tmp')
+    >>> dir.mkdir(exist_ok=True)
+    >>> train, test = Downloader().usps(dir)
+    """
+
+    def mnist(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]:
+        def _chech_is_downloaded(path: Path) -> bool:
+            path = path / "MNIST"
+            if path.exists():
+                required_files = [MNIST_SOURCE[key][1] for key in MNIST_SOURCE.keys()]
+                if all([(path / file).exists() for file in required_files]):
+                    print("MNIST Dataset already downloaded.")
+                    return True
+                else:
+                    return False
+            else:
+                path.mkdir(parents=True, exist_ok=True)
+                return False
+
+        def _download_data(path: Path) -> None:
+            urls = {key: MNIST_SOURCE[key][0] for key in MNIST_SOURCE.keys()}
+
+            for name, url in urls.items():
+                file_path = os.path.join(path, url.split("/")[-1])
+                if not os.path.exists(
+                    file_path.replace(".gz", "")
+                ):  # Avoid re-downloading
+                    urlretrieve(url, file_path, reporthook=self.__reporthook)
+                    with gzip.open(file_path, "rb") as f_in:
+                        with open(file_path.replace(".gz", ""), "wb") as f_out:
+                            f_out.write(f_in.read())
+                    os.remove(file_path)  # Remove compressed file
+
+        def _get_labels(path: Path) -> np.ndarray:
+            with open(path, "rb") as f:
+                data = np.frombuffer(f.read(), dtype=np.uint8, offset=8)
+            return data
+
+        if not _chech_is_downloaded(data_dir):
+            _download_data(data_dir)
+
+        train_labels_path = data_dir / "MNIST" / MNIST_SOURCE["train_labels"][1]
+        test_labels_path = data_dir / "MNIST" / MNIST_SOURCE["test_labels"][1]
+
+        train_labels = _get_labels(train_labels_path)
+        test_labels = _get_labels(test_labels_path)
+
+        return train_labels, test_labels
+
+    def svhn(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]:
+        raise NotImplementedError("SVHN download not implemented yet")
+
+    def usps(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Download the USPS dataset and save it as an HDF5 file to `data_dir/usps.h5`.
+        """
+
+        def already_downloaded(path):
+            if not path.exists() or not path.is_file():
+                return False
+
+            with h5.File(path, "r") as f:
+                return "train" in f and "test" in f
+
+        filename = data_dir / "usps.h5"
+
+        if already_downloaded(filename):
+            with h5.File(filename, "r") as f:
+                return f["train"]["target"][:], f["test"]["target"][:]
+
+        url_train, _, train_md5 = USPS_SOURCE["train"]
+        url_test, _, test_md5 = USPS_SOURCE["test"]
+
+        # Using temporary directory ensures temporary files are deleted after use
+        with TemporaryDirectory() as tmp_dir:
+            train_path = Path(tmp_dir) / "train"
+            test_path = Path(tmp_dir) / "test"
+
+            # Download the dataset and report the progress
+            urlretrieve(url_train, train_path, reporthook=self.__reporthook)
+            self.__check_integrity(train_path, train_md5)
+            train_targets = self.__extract_usps(train_path, filename, "train")
+
+            urlretrieve(url_test, test_path, reporthook=self.__reporthook)
+            self.__check_integrity(test_path, test_md5)
+            test_targets = self.__extract_usps(test_path, filename, "test")
+
+        return train_targets, test_targets
+
+    def __extract_usps(self, src: Path, dest: Path, mode: str):
+        # Load the dataset and save it as an HDF5 file
+        with bz2.open(src) as fp:
+            raw = [line.decode().split() for line in fp.readlines()]
+
+            tmp = [[x.split(":")[-1] for x in data[1:]] for data in raw]
+
+            imgs = np.asarray(tmp, dtype=np.float32)
+            imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8)
+
+            targets = [int(d[0]) - 1 for d in raw]
+
+        with h5.File(dest, "a") as f:
+            f.create_dataset(f"{mode}/data", data=imgs, dtype=np.float32)
+            f.create_dataset(f"{mode}/target", data=targets, dtype=np.int32)
+
+        return targets
+
+    @staticmethod
+    def __reporthook(blocknum, blocksize, totalsize):
+        """
+        Use this function to report download progress
+        for the urllib.request.urlretrieve function.
+        """
+
+        denom = 1024 * 1024
+        readsofar = blocknum * blocksize
+
+        if totalsize > 0:
+            percent = readsofar * 1e2 / totalsize
+            s = f"\r{int(percent):^3}% {readsofar / denom:.2f} of {totalsize / denom:.2f} MB"
+            print(s, end="", flush=True)
+            if readsofar >= totalsize:
+                print()
+
+    @staticmethod
+    def __check_integrity(filepath, checksum):
+        """Check the integrity of the USPS dataset file.
+
+        Args
+        ----
+        filepath : pathlib.Path
+            Path to the USPS dataset file.
+        checksum : str
+            MD5 checksum of the dataset file.
+
+        Returns
+        -------
+        bool
+            True if the checksum of the file matches the expected checksum, False otherwise
+        """
+
+        file_hash = hashlib.md5(filepath.read_bytes()).hexdigest()
+
+        if not checksum == file_hash:
+            raise ValueError(
+                f"File integrity check failed. Expected {checksum}, got {file_hash}"
+            )
diff --git a/utils/dataloaders/mnist_0_3.py b/utils/dataloaders/mnist_0_3.py
index bad3bd9..52a5a28 100644
--- a/utils/dataloaders/mnist_0_3.py
+++ b/utils/dataloaders/mnist_0_3.py
@@ -1,137 +1,77 @@
-import gzip
-import os
-import urllib.request
 from pathlib import Path
 
 import numpy as np
 from torch.utils.data import Dataset
 
+from .datasources import MNIST_SOURCE
+
 
 class MNISTDataset0_3(Dataset):
     """
-    A custom dataset class for loading MNIST data, specifically for digits 0 through 3.
-
+    A custom Dataset class for loading a subset of the MNIST dataset containing digits 0 to 3.
     Parameters
     ----------
     data_path : Path
-        The root directory where the MNIST data is stored or will be downloaded.
+        The root directory where the MNIST data is stored.
+    sample_ids : list
+        A list of indices specifying which samples to load.
     train : bool, optional
-        If True, loads the training data, otherwise loads the test data. Default is False.
+        If True, load training data, otherwise load test data. Default is False.
     transform : callable, optional
-        A function/transform that takes in an image and returns a transformed version. Default is None.
-    download : bool, optional
-        If True, downloads the dataset if it is not already present in the specified data_path. Default is False.
-
+        A function/transform to apply to the images. Default is None.
     Attributes
     ----------
     data_path : Path
         The root directory where the MNIST data is stored.
     mnist_path : Path
-        The directory where the MNIST data files are stored.
+        The directory where the MNIST dataset is located within the root directory.
+    idx : list
+        A list of indices specifying which samples to load.
     train : bool
-        Indicates whether the training data or test data is being used.
+        Indicates whether to load training data or test data.
     transform : callable
-        A function/transform that takes in an image and returns a transformed version.
-    download : bool
-        Indicates whether the dataset should be downloaded if not present.
+        A function/transform to apply to the images.
+    num_classes : int
+        The number of classes in the dataset (0 to 3).
     images_path : Path
-        The path to the image file (training or test) based on the `train` flag.
+        The path to the image file (train or test) based on the `train` flag.
     labels_path : Path
-        The path to the label file (training or test) based on the `train` flag.
-    idx : numpy.ndarray
-        Indices of the labels that are less than 4.
+        The path to the label file (train or test) based on the `train` flag.
     length : int
         The number of samples in the dataset.
-
     Methods
     -------
-    _parse_labels(train)
-        Parses the labels from the label file.
-    _chech_is_downloaded()
-        Checks if the dataset is already downloaded.
-    _download_data()
-        Downloads and extracts the MNIST dataset.
     __len__()
         Returns the number of samples in the dataset.
     __getitem__(index)
-        Returns the image and label at the specified index.
+        Retrieves the image and label at the specified index.
     """
 
     def __init__(
         self,
         data_path: Path,
+        sample_ids: list,
         train: bool = False,
         transform=None,
-        download: bool = False,
     ):
         super().__init__()
 
         self.data_path = data_path
         self.mnist_path = self.data_path / "MNIST"
+        self.idx = sample_ids
         self.train = train
         self.transform = transform
-        self.download = download
         self.num_classes = 4
 
-        if not self.download and not self._chech_is_downloaded():
-            raise ValueError(
-                "Data not found. Set --download-data=True to download the data."
-            )
-        if self.download and not self._chech_is_downloaded():
-            self._download_data()
-
         self.images_path = self.mnist_path / (
-            "train-images-idx3-ubyte" if train else "t10k-images-idx3-ubyte"
+            MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1]
         )
         self.labels_path = self.mnist_path / (
-            "train-labels-idx1-ubyte" if train else "t10k-labels-idx1-ubyte"
+            MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1]
         )
 
-        labels = self._parse_labels(train=self.train)
-
-        self.idx = np.where(labels < 4)[0]
-
         self.length = len(self.idx)
 
-    def _parse_labels(self, train):
-        with open(self.labels_path, "rb") as f:
-            data = np.frombuffer(f.read(), dtype=np.uint8, offset=8)
-        return data
-
-    def _chech_is_downloaded(self):
-        if self.mnist_path.exists():
-            required_files = [
-                "train-images-idx3-ubyte",
-                "train-labels-idx1-ubyte",
-                "t10k-images-idx3-ubyte",
-                "t10k-labels-idx1-ubyte",
-            ]
-            if all([(self.mnist_path / file).exists() for file in required_files]):
-                print("MNIST Dataset already downloaded.")
-                return True
-            else:
-                return False
-        else:
-            self.mnist_path.mkdir(parents=True, exist_ok=True)
-            return False
-
-    def _download_data(self):
-        urls = {
-            "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz",
-            "train_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz",
-            "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz",
-            "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz",
-        }
-
-        for name, url in urls.items():
-            file_path = os.path.join(self.mnist_path, url.split("/")[-1])
-            if not os.path.exists(file_path.replace(".gz", "")):  # Avoid re-downloading
-                urllib.request.urlretrieve(url, file_path)
-                with gzip.open(file_path, "rb") as f_in:
-                    with open(file_path.replace(".gz", ""), "wb") as f_out:
-                        f_out.write(f_in.read())
-                os.remove(file_path)  # Remove compressed file
-
     def __len__(self):
         return self.length
 
diff --git a/utils/dataloaders/mnist_4_9.py b/utils/dataloaders/mnist_4_9.py
new file mode 100644
index 0000000..5de281b
--- /dev/null
+++ b/utils/dataloaders/mnist_4_9.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+
+import numpy as np
+from torch.utils.data import Dataset
+
+from .datasources import MNIST_SOURCE
+
+
+class MNISTDataset4_9(Dataset):
+    """
+    MNIST dataset of numbers 4-9.
+
+    Parameters
+    ----------
+    data_path : Path
+        Root directory where MNIST dataset is stored
+    sample_ids : np.ndarray
+        Array of indices spcifying which samples to load. This determines the samples used by the dataloader.
+    train : bool, optional
+        Whether to train the model or not, by default False
+    """
+
+    def __init__(self, data_path: Path, sample_ids: np.ndarray, train: bool = False):
+        super.__init__()
+        self.data_path = data_path
+        self.mnist_path = self.data_path / "MNIST"
+        self.samples = sample_ids
+        self.train = train
+
+        self.images_path = self.mnist_path / (
+            MNIST_SOURCE["train_images"][1] if train else MNIST_SOURCE["test_images"][1]
+        )
+        self.labels_path = self.mnist_path / (
+            MNIST_SOURCE["train_labels"][1] if train else MNIST_SOURCE["test_labels"][1]
+        )
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        with open(self.labels_path, "rb") as labelfile:
+            label_pos = 8 + self.sample[idx]
+            labelfile.seek(label_pos)
+            label = int.from_bytes(labelfile.read(1), byteorder="big")
+
+        with open(self.images_path, "rb") as imagefile:
+            image_pos = 16 + self.samples[idx] * 28 * 28
+            imagefile.seek(image_pos)
+            image = np.frombuffer(imagefile.read(28 * 28), dtype=np.uint8).reshape(
+                28, 28
+            )
+
+        image = np.expand_dims(image, axis=0)  # Channel
+
+        return image, label
diff --git a/utils/dataloaders/svhn.py b/utils/dataloaders/svhn.py
index e71de73..f0bd18c 100644
--- a/utils/dataloaders/svhn.py
+++ b/utils/dataloaders/svhn.py
@@ -1,4 +1,5 @@
 import os
+
 import numpy as np
 from scipy.io import loadmat
 from torch.utils.data import Dataset
@@ -7,13 +8,13 @@
 
 class SVHNDataset(Dataset):
     def __init__(
-        self, 
-        data_path: str, 
+        self,
+        data_path: str,
         train: bool,
-        transform=None, 
-        download:bool=True, 
-        nr_channels=3
-        ):
+        transform=None,
+        download: bool = True,
+        nr_channels=3,
+    ):
         """
         Initializes the SVHNDataset object.
         Args:
@@ -26,8 +27,8 @@ def __init__(
         """
         super().__init__()
         # assert split == "train" or split == "test"
-        self.split = 'train' if train else 'test'
-    
+        self.split = "train" if train else "test"
+
         if download:
             self._download_data(data_path)
 
@@ -37,7 +38,7 @@ def __init__(
         self.images = data["X"].transpose(3, 1, 0, 2)
         self.labels = data["y"].flatten()
         self.labels[self.labels == 10] = 0
-        
+
         self.nr_channels = nr_channels
         self.transforms = transform
 
@@ -49,7 +50,7 @@ def _download_data(self, path: str):
             split (str): The dataset split to download, either 'train' or 'test'.
         """
         print(f"Downloading SVHN data into {path}")
-        
+
         SVHN(path, split=self.split, download=True)
 
     def __len__(self):
@@ -72,7 +73,7 @@ def __getitem__(self, index):
 
         if self.nr_channels == 1:
             img = np.mean(img, axis=2, keepdims=True)
-        
+
         if self.transforms is not None:
             img = self.transforms(img)
 
diff --git a/utils/dataloaders/usps_0_6.py b/utils/dataloaders/usps_0_6.py
index 3673fa9..70286dc 100644
--- a/utils/dataloaders/usps_0_6.py
+++ b/utils/dataloaders/usps_0_6.py
@@ -4,19 +4,12 @@
 This module contains the Dataset class for the USPS dataset with labels 0-6.
 """
 
-import bz2
-import hashlib
 from pathlib import Path
-from tempfile import TemporaryDirectory
-from urllib.request import urlretrieve
 
 import h5py as h5
 import numpy as np
 from PIL import Image
 from torch.utils.data import Dataset
-from torchvision import transforms
-
-from .datasources import USPS_SOURCE
 
 
 class USPSDataset0_6(Dataset):
@@ -87,9 +80,9 @@ class USPSDataset0_6(Dataset):
     def __init__(
         self,
         data_path: Path,
+        sample_ids: list,
         train: bool = False,
         transform=None,
-        download: bool = False,
     ):
         super().__init__()
 
@@ -97,168 +90,21 @@ def __init__(
         self.filepath = path / self.filename
         self.transform = transform
         self.mode = "train" if train else "test"
+        self.sample_ids = sample_ids
 
-        # Download the dataset if it does not exist in a temporary directory
-        # to automatically clean up the downloaded file
-        if download and not self._dataset_ok():
-            url, _, checksum = USPS_SOURCE[self.mode]
-
-            print(f"Downloading USPS dataset ({self.mode})...")
-            self.download(url, self.filepath, checksum, self.mode)
-
-        self.idx = self._index()
-
-    def _dataset_ok(self):
-        """Check if the dataset file exists and contains the required datasets."""
-
-        if not self.filepath.exists():
-            print(f"Dataset file {self.filepath} does not exist.")
-            return False
-
-        with h5.File(self.filepath, "r") as f:
-            for mode in ["train", "test"]:
-                if mode not in f:
-                    print(
-                        f"Dataset file {self.filepath} is missing the {mode} dataset."
-                    )
-                    return False
-
-        return True
-
-    def download(self, url, filepath, checksum, mode):
-        """Download the USPS dataset, and save it as an HDF5 file.
-
-        Args
-        ----
-        url : str
-            URL to download the dataset from.
-        filepath : pathlib.Path
-            Path to save the downloaded dataset.
-        checksum : str
-            MD5 checksum of the downloaded file.
-        mode : str
-            Mode of the dataset, either train or test.
-
-        Raises
-        ------
-        ValueError
-            If the checksum of the downloaded file does not match the expected checksum.
-        """
-
-        def reporthook(blocknum, blocksize, totalsize):
-            """Report download progress."""
-            denom = 1024 * 1024
-            readsofar = blocknum * blocksize
-            if totalsize > 0:
-                percent = readsofar * 1e2 / totalsize
-                s = f"\r{int(percent):^3}% {readsofar / denom:.2f} of {totalsize / denom:.2f} MB"
-                print(s, end="", flush=True)
-                if readsofar >= totalsize:
-                    print()
-
-        # Download the dataset to a temporary file
-        with TemporaryDirectory() as tmpdir:
-            tmpdir = Path(tmpdir)
-            tmpfile = tmpdir / "usps.bz2"
-            urlretrieve(
-                url,
-                tmpfile,
-                reporthook=reporthook,
-            )
-
-            # For fun we can check the integrity of the downloaded file
-            if not self.check_integrity(tmpfile, checksum):
-                errmsg = (
-                    "The checksum of the downloaded file does "
-                    "not match the expected checksum."
-                )
-                raise ValueError(errmsg)
-
-            # Load the dataset and save it as an HDF5 file
-            with bz2.open(tmpfile) as fp:
-                raw = [line.decode().split() for line in fp.readlines()]
-
-                tmp = [[x.split(":")[-1] for x in data[1:]] for data in raw]
-
-                imgs = np.asarray(tmp, dtype=np.float32)
-                imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8)
-
-                targets = [int(d[0]) - 1 for d in raw]
-
-        with h5.File(self.filepath, "a") as f:
-            f.create_dataset(f"{mode}/data", data=imgs, dtype=np.float32)
-            f.create_dataset(f"{mode}/target", data=targets, dtype=np.int32)
-
-    @staticmethod
-    def check_integrity(filepath, checksum):
-        """Check the integrity of the USPS dataset file.
-
-        Args
-        ----
-        filepath : pathlib.Path
-            Path to the USPS dataset file.
-        checksum : str
-            MD5 checksum of the dataset file.
-
-        Returns
-        -------
-        bool
-            True if the checksum of the file matches the expected checksum, False otherwise
-        """
-
-        file_hash = hashlib.md5(filepath.read_bytes()).hexdigest()
-
-        return checksum == file_hash
-
-    def _index(self):
-        with h5.File(self.filepath, "r") as f:
-            labels = f[self.mode]["target"][:]
-
-        # Get indices of samples with labels 0-6
-        mask = labels <= 6
-        idx = np.where(mask)[0]
+    def __len__(self):
+        return len(self.sample_ids)
 
-        return idx
+    def __getitem__(self, id):
+        index = self.sample_ids[id]
 
-    def _load_data(self, idx):
         with h5.File(self.filepath, "r") as f:
-            data = f[self.mode]["data"][idx].astype(np.uint8)
-            label = f[self.mode]["target"][idx]
+            data = f[self.mode]["data"][index].astype(np.uint8)
+            label = f[self.mode]["target"][index]
 
-        return data, label
-
-    def __len__(self):
-        return len(self.idx)
-
-    def __getitem__(self, idx):
-        data, target = self._load_data(self.idx[idx])
         data = Image.fromarray(data, mode="L")
 
-        # one hot encode the target
-        target = np.eye(self.num_classes, dtype=np.float32)[target]
-
         if self.transform:
             data = self.transform(data)
 
-        return data, target
-
-
-if __name__ == "__main__":
-    # Example usage:
-    transform = transforms.Compose(
-        [
-            transforms.Resize((16, 16)),
-            transforms.ToTensor(),
-        ]
-    )
-
-    dataset = USPSDataset0_6(
-        data_path="data",
-        train=True,
-        download=False,
-        transform=transform,
-    )
-    print(len(dataset))
-    data, target = dataset[0]
-    print(data.shape)
-    print(target)
+        return data, label
diff --git a/utils/load_data.py b/utils/load_data.py
index 8ebbec5..a3bfe42 100644
--- a/utils/load_data.py
+++ b/utils/load_data.py
@@ -1,11 +1,22 @@
-from torch.utils.data import Dataset
+import numpy as np
+from torch.utils.data import random_split
 
-from .dataloaders import MNISTDataset0_3, USPSDataset0_6, USPSH5_Digit_7_9_Dataset, SVHNDataset
+from .dataloaders import (
+    Downloader,
+    MNISTDataset0_3,
+    SVHNDataset,
+    USPSDataset0_6,
+    USPSH5_Digit_7_9_Dataset,
+)
 
 
-def load_data(dataset: str, *args, **kwargs) -> Dataset:
+def filter_labels(samples: list, wanted_labels: list) -> list:
+    return list(filter(lambda x: x in wanted_labels, samples))
+
+
+def load_data(dataset: str, *args, **kwargs) -> tuple:
     """
-    Load the dataset based on the dataset name.
+    load the dataset based on the dataset name.
 
     Args
     ----
@@ -18,8 +29,8 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset:
 
     Returns
     -------
-    dataset : torch.utils.data.Dataset
-        Dataset object.
+    tuple
+        Tuple of train, validation and test datasets.
 
     Raises
     ------
@@ -28,21 +39,67 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset:
 
     Examples
     --------
-    >>> from utils import load_data
-    >>> dataset = load_data("usps_0-6", data_path="data", train=True, download=True)
-    >>> len(dataset)
-    5460
+    >>> from utils import setup_data
+    >>> train, val, test = setup_data("usps_0-6", data_path="data", train=True, download=True)
+    >>> len(train), len(val), len(test)
+    (4914, 546, 1782)
     """
+    downloader = Downloader()
+    data_dir = kwargs.get("data_dir")
+    transform = kwargs.get("transform")
     match dataset.lower():
         case "usps_0-6":
-            return USPSDataset0_6(*args, **kwargs)
-        case "mnist_0-3":
-            return MNISTDataset0_3(*args, **kwargs)
+            dataset = USPSDataset0_6
+            train_labels, test_labels = downloader.usps(data_dir=data_dir)
+            labels = np.arange(7)
         case "usps_7-9":
-            return USPSH5_Digit_7_9_Dataset(*args, **kwargs)
+            dataset = USPSH5_Digit_7_9_Dataset
+            train_labels, test_labels = downloader.usps(data_dir=data_dir)
+            labels = np.arange(7, 10)
+        case "mnist_0-3":
+            dataset = MNISTDataset0_3
+            train_labels, test_labels = downloader.mnist(data_dir=data_dir)
+            labels = np.arange(4)
         case "svhn":
-            return SVHNDataset(*args, **kwargs)
+            dataset = SVHNDataset
+            train_labels, test_labels = downloader.svhn(data_dir=data_dir)
+            labels = np.arange(10)
         case "mnist_4-9":
             raise NotImplementedError("MNIST 4-9 dataset not yet implemented.")
         case _:
             raise NotImplementedError(f"Dataset: {dataset} not implemented.")
+
+    val_size = kwargs.get("val_size", 0.2)
+
+    # Get the indices of the samples
+    train_indices = np.arange(len(train_labels))
+    test_indices = np.arange(len(test_labels))
+
+    # Filter the labels to only get indices of the wanted labels
+    train_samples = train_indices[np.isin(train_labels, labels)]
+    test_samples = test_indices[np.isin(test_labels, labels)]
+
+    train_samples, val_samples = random_split(train_samples, [1 - val_size, val_size])
+
+    train = dataset(
+        data_path=data_dir,
+        sample_ids=train_samples,
+        train=True,
+        transform=transform,
+    )
+
+    val = dataset(
+        data_path=data_dir,
+        sample_ids=val_samples,
+        train=True,
+        transform=transform,
+    )
+
+    test = dataset(
+        data_path=data_dir,
+        sample_ids=test_samples,
+        train=False,
+        transform=transform,
+    )
+
+    return train, val, test
diff --git a/utils/load_metric.py b/utils/load_metric.py
index a321845..e56f291 100644
--- a/utils/load_metric.py
+++ b/utils/load_metric.py
@@ -45,10 +45,13 @@ class MetricWrapper(nn.Module):
     {'entropy': [], 'f1': [], 'precision': []}
     """
 
-    def __init__(self, *metrics, num_classes):
+    def __init__(self, *metrics, num_classes, macro_averaging=False, **kwargs):
         super().__init__()
         self.metrics = {}
-        self.num_classes = num_classes
+        self.params = {
+            "num_classes": num_classes,
+            "macro_averaging": macro_averaging,
+        } | kwargs
 
         for metric in metrics:
             self.metrics[metric] = self._get_metric(metric)
@@ -72,15 +75,15 @@ def _get_metric(self, key):
 
         match key.lower():
             case "entropy":
-                return EntropyPrediction(num_classes=self.num_classes)
+                return EntropyPrediction(**self.params)
             case "f1":
-                return F1Score(num_classes=self.num_classes)
+                return F1Score(**self.params)
             case "recall":
-                return Recall(num_classes=self.num_classes)
+                return Recall(**self.params)
             case "precision":
-                return Precision(num_classes=self.num_classes)
+                return Precision(**self.params)
             case "accuracy":
-                return Accuracy(num_classes=self.num_classes)
+                return Accuracy(**self.params)
             case _:
                 raise ValueError(f"Metric {key} not supported")
 
diff --git a/utils/metrics/F1.py b/utils/metrics/F1.py
index 1e0e795..70791c5 100644
--- a/utils/metrics/F1.py
+++ b/utils/metrics/F1.py
@@ -4,29 +4,39 @@
 
 class F1Score(nn.Module):
     """
-    F1 Score implementation with direct averaging inside the compute method.
+    F1 Score implementation with support for both macro and micro averaging.
+
+    This class computes the F1 score during training using either macro or micro averaging.
+    The F1 score is calculated based on the true positives (TP), false positives (FP),
+    and false negatives (FN) for each class.
 
     Parameters
     ----------
     num_classes : int
-        Number of classes.
+        The number of classes in the classification task.
+
+    macro_averaging : bool, optional, default=False
+        If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score.
 
     Attributes
     ----------
     num_classes : int
-        The number of classes.
+        The number of classes in the classification task.
 
     tp : torch.Tensor
-        Tensor for True Positives (TP) for each class.
+        Tensor storing the count of True Positives (TP) for each class.
 
     fp : torch.Tensor
-        Tensor for False Positives (FP) for each class.
+        Tensor storing the count of False Positives (FP) for each class.
 
     fn : torch.Tensor
-        Tensor for False Negatives (FN) for each class.
+        Tensor storing the count of False Negatives (FN) for each class.
+
+    macro_averaging : bool
+        A flag indicating whether to compute the macro-averaged F1 score or not.
     """
 
-    def __init__(self, num_classes):
+    def __init__(self, num_classes, macro_averaging=False):
         """
         Initializes the F1Score object, setting up the necessary state variables.
 
@@ -35,30 +45,93 @@ def __init__(self, num_classes):
         num_classes : int
             The number of classes in the classification task.
 
+        macro_averaging : bool, optional, default=False
+            If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score.
         """
-
         super().__init__()
 
         self.num_classes = num_classes
+        self.macro_averaging = macro_averaging
 
-        # Initialize  variables for True Positives (TP), False Positives (FP), and False Negatives (FN)
+        # Initialize variables for True Positives (TP), False Positives (FP), and False Negatives (FN)
         self.tp = torch.zeros(num_classes)
         self.fp = torch.zeros(num_classes)
         self.fn = torch.zeros(num_classes)
 
-    def update(self, preds, target):
+    def _micro_F1(self):
+        """
+        Compute the Micro F1 score by aggregating TP, FP, and FN across all classes.
+
+        Micro F1 score is calculated globally by considering all predictions together, regardless of class.
+
+        Returns
+        -------
+        torch.Tensor
+            The micro-averaged F1 score.
+        """
+        tp = torch.sum(self.tp)
+        fp = torch.sum(self.fp)
+        fn = torch.sum(self.fn)
+
+        precision = tp / (tp + fp + 1e-8)  # Avoid division by zero
+        recall = tp / (tp + fn + 1e-8)  # Avoid division by zero
+
+        f1 = (
+            2 * precision * recall / (precision + recall + 1e-8)
+        )  # Avoid division by zero
+        return f1
+
+    def _macro_F1(self):
+        """
+        Compute the Macro F1 score by calculating the F1 score per class and averaging.
+
+        Macro F1 score is calculated as the average of per-class F1 scores. This approach treats all classes equally,
+        regardless of their frequency.
+
+        Returns
+        -------
+        torch.Tensor
+            The macro-averaged F1 score.
+        """
+        precision_per_class = self.tp / (
+            self.tp + self.fp + 1e-8
+        )  # Avoid division by zero
+        recall_per_class = self.tp / (
+            self.tp + self.fn + 1e-8
+        )  # Avoid division by zero
+        f1_per_class = (
+            2
+            * precision_per_class
+            * recall_per_class
+            / (precision_per_class + recall_per_class + 1e-8)
+        )  # Avoid division by zero
+
+        # Take the average of F1 scores across all classes
+        f1_score = torch.mean(f1_per_class)
+        return f1_score
+
+    def forward(self, preds, target):
         """
-        Update the variables with predictions and true labels.
+        Update the True Positives, False Positives, and False Negatives, and compute the F1 score.
+
+        This method computes the F1 score based on the predictions and true labels. It can compute either the
+        macro-averaged or micro-averaged F1 score, depending on the `macro_averaging` flag.
 
         Parameters
         ----------
         preds : torch.Tensor
-            Predicted logits (shape: [batch_size, num_classes]).
+            Predicted logits or class indices (shape: [batch_size, num_classes]).
+            These logits are typically the output of a softmax or sigmoid activation.
 
         target : torch.Tensor
-            True labels (shape: [batch_size]).
+            True labels (shape: [batch_size]), where each element is an integer representing the true class.
+
+        Returns
+        -------
+        torch.Tensor
+            The computed F1 score (either micro or macro, based on `macro_averaging`).
         """
-        preds = torch.argmax(preds, dim=1)
+        preds = torch.argmax(preds, dim=-1)
 
         # Calculate True Positives (TP), False Positives (FP), and False Negatives (FN) per class
         for i in range(self.num_classes):
@@ -66,21 +139,11 @@ def update(self, preds, target):
             self.fp[i] += torch.sum((preds == i) & (target != i)).float()
             self.fn[i] += torch.sum((preds != i) & (target == i)).float()
 
-    def compute(self):
-        """
-        Compute the F1 score.
-
-        Returns
-        -------
-        torch.Tensor
-           The computed F1 score.
-        """
-
-        # Compute F1 score based on the specified averaging method
-        f1_score = (
-            2
-            * torch.sum(self.tp)
-            / (2 * torch.sum(self.tp) + torch.sum(self.fp) + torch.sum(self.fn))
-        )
+        if self.macro_averaging:
+            # Calculate Macro F1 score
+            f1_score = self._macro_F1()
+        else:
+            # Calculate Micro F1 score
+            f1_score = self._micro_F1()
 
         return f1_score
diff --git a/utils/metrics/accuracy.py b/utils/metrics/accuracy.py
index 4d1cdd1..5123d36 100644
--- a/utils/metrics/accuracy.py
+++ b/utils/metrics/accuracy.py
@@ -3,9 +3,10 @@
 
 
 class Accuracy(nn.Module):
-    def __init__(self, num_classes):
+    def __init__(self, num_classes, macro_averaging=False):
         super().__init__()
         self.num_classes = num_classes
+        self.macro_averaging = macro_averaging
 
     def forward(self, y_true, y_pred):
         """
@@ -23,12 +24,79 @@ def forward(self, y_true, y_pred):
         float
             Accuracy score.
         """
+        if y_pred.dim() > 1:
+            y_pred = y_pred.argmax(dim=1)
+        if self.macro_averaging:
+            return self._macro_acc(y_true, y_pred)
+        else:
+            return self._micro_acc(y_true, y_pred)
+
+    def _macro_acc(self, y_true, y_pred):
+        """
+        Compute the macro-average accuracy.
+
+        Parameters
+        ----------
+        y_true : torch.Tensor
+            True labels.
+        y_pred : torch.Tensor
+            Predicted labels.
+
+        Returns
+        -------
+        float
+            Macro-average accuracy score.
+        """
+        y_true, y_pred = y_true.flatten(), y_pred.flatten()  # Ensure 1D shape
+
+        classes = torch.unique(y_true)  # Find unique class labels
+        acc_per_class = []
+
+        for c in classes:
+            mask = y_true == c  # Mask for class c
+            acc = (y_pred[mask] == y_true[mask]).float().mean()  # Accuracy for class c
+            acc_per_class.append(acc)
+
+        macro_acc = torch.stack(acc_per_class).mean().item()  # Average across classes
+        return macro_acc
+
+    def _micro_acc(self, y_true, y_pred):
+        """
+        Compute the micro-average accuracy.
+
+        Parameters
+        ----------
+        y_true : torch.Tensor
+            True labels.
+        y_pred : torch.Tensor
+            Predicted labels.
+
+        Returns
+        -------
+        float
+            Micro-average accuracy score.
+        """
         return (y_true == y_pred).float().mean().item()
 
 
 if __name__ == "__main__":
+    accuracy = Accuracy(5)
+    macro_accuracy = Accuracy(5, macro_averaging=True)
+
     y_true = torch.tensor([0, 3, 2, 3, 4])
     y_pred = torch.tensor([0, 1, 2, 3, 4])
-
-    accuracy = Accuracy()
     print(accuracy(y_true, y_pred))
+    print(macro_accuracy(y_true, y_pred))
+
+    y_true = torch.tensor([0, 3, 2, 3, 4])
+    y_onehot_pred = torch.tensor(
+        [
+            [1, 0, 0, 0, 0],
+            [0, 1, 0, 0, 0],
+            [0, 0, 1, 0, 0],
+            [0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 1],
+        ]
+    )
+    print(accuracy(y_true, y_onehot_pred))
+    print(macro_accuracy(y_true, y_onehot_pred))
diff --git a/utils/metrics/precision.py b/utils/metrics/precision.py
index 61ba1eb..a596df7 100644
--- a/utils/metrics/precision.py
+++ b/utils/metrics/precision.py
@@ -1,10 +1,6 @@
 import torch
 import torch.nn as nn
 
-USE_MEAN = True
-
-# Precision = TP / (TP + FP)
-
 
 class Precision(nn.Module):
     """Metric module for precision. Can calculate precision both as a mean of precisions or as brute function of true positives and false positives.
@@ -13,49 +9,97 @@ class Precision(nn.Module):
     ----------
     num_classes : int
         Number of classes in the dataset.
-    use_mean : bool
-        Whether to calculate precision as a mean of precisions or as a brute function of true positives and false positives.
+    micro_averaging : bool
+        Wheter to compute the micro or macro precision (default False)
     """
 
-    def __init__(self, num_classes: int, use_mean: bool = True):
+    def __init__(self, num_classes: int, macro_averaging: bool = False):
         super().__init__()
 
         self.num_classes = num_classes
-        self.use_mean = use_mean
+        self.macro_averaging = macro_averaging
+
+    def forward(self, y_true: torch.tensor, logits: torch.tensor) -> torch.tensor:
+        """Compute precision of model
+
+        Parameters
+        ----------
+        y_true : torch.tensor
+            True labels
+        y_pred : torch.tensor
+            Predicted labels
 
-    def forward(self, y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor:
-        """Calculates the precision score given number of classes and the true and predicted labels.
+        Returns
+        -------
+        torch.tensor
+            Precision score
+        """
+        y_pred = logits.argmax(dim=-1)
+        return (
+            self._macro_avg_precision(y_true, y_pred)
+            if self.macro_averaging
+            else self._micro_avg_precision(y_true, y_pred)
+        )
+
+    def _micro_avg_precision(
+        self, y_true: torch.tensor, y_pred: torch.tensor
+    ) -> torch.tensor:
+        """Compute micro-average precision by first calculating true/false positive across all classes and then find the precision.
 
         Parameters
         ----------
         y_true : torch.tensor
-            true labels
+            True labels
         y_pred : torch.tensor
-            predicted labels
+            Predicted labels
 
         Returns
         -------
         torch.tensor
-            precision score
+            Micro-averaged precision
         """
-        # One-hot encode the target tensor
+        print(y_true.shape)
         true_oh = torch.zeros(y_true.size(0), self.num_classes).scatter_(
             1, y_true.unsqueeze(1), 1
         )
         pred_oh = torch.zeros(y_pred.size(0), self.num_classes).scatter_(
             1, y_pred.unsqueeze(1), 1
         )
+        tp = torch.sum(true_oh * pred_oh)
+        fp = torch.sum(~true_oh[pred_oh.bool()].bool())
+
+        return torch.nanmean(tp / (tp + fp))
 
-        if self.use_mean:
-            tp = torch.sum(true_oh * pred_oh, 0)
-            fp = torch.sum(~true_oh.bool() * pred_oh, 0)
+    def _macro_avg_precision(
+        self, y_true: torch.tensor, y_pred: torch.tensor
+    ) -> torch.tensor:
+        """Compute macro-average precision by finding true/false positives of each class separately then averaging across all classes.
 
-        else:
-            tp = torch.sum(true_oh * pred_oh)
-            fp = torch.sum(~true_oh[pred_oh.bool()].bool())
+        Parameters
+        ----------
+        y_true : torch.tensor
+            True labels
+        y_pred : torch.tensor
+            Predicted labels
+
+        Returns
+        -------
+        torch.tensor
+            Macro-averaged precision
+        """
+        true_oh = torch.zeros(y_true.size(0), self.num_classes).scatter_(
+            1, y_true.unsqueeze(1), 1
+        )
+        pred_oh = torch.zeros(y_pred.size(0), self.num_classes).scatter_(
+            1, y_pred.unsqueeze(1), 1
+        )
+        tp = torch.sum(true_oh * pred_oh, 0)
+        fp = torch.sum(~true_oh.bool() * pred_oh, 0)
 
         return torch.nanmean(tp / (tp + fp))
 
 
 if __name__ == "__main__":
-    pass
+    print(
+        "Congratulations, you succesfully ran the Precision metric class. You should be proud of this marvelous achievement!"
+    )
diff --git a/utils/metrics/recall.py b/utils/metrics/recall.py
index ab9ae16..80a1b72 100644
--- a/utils/metrics/recall.py
+++ b/utils/metrics/recall.py
@@ -2,12 +2,12 @@
 import torch.nn as nn
 
 
-def one_hot_encode(y_true, num_classes):
+def one_hot_encode(vec, num_classes):
     """One-hot encode the target tensor.
 
     Args
     ----
-    y_true : torch.Tensor
+    vec : torch.Tensor
         Target tensor.
     num_classes : int
         Number of classes in the dataset.
@@ -18,25 +18,65 @@ def one_hot_encode(y_true, num_classes):
         One-hot encoded tensor.
     """
 
-    y_onehot = torch.zeros(y_true.size(0), num_classes)
-    y_onehot.scatter_(1, y_true.unsqueeze(1), 1)
-    return y_onehot
+    onehot = torch.zeros(vec.size(0), num_classes)
+    onehot.scatter_(1, vec.unsqueeze(1), 1)
+    return onehot
 
 
 class Recall(nn.Module):
-    def __init__(self, num_classes):
-        super().__init__()
+    """
+    Recall metric.
+
+    Args
+    ----
+    num_classes : int
+        Number of classes in the dataset.
+    macro_averaging : bool
+        If True, calculate the recall for each class and return the average.
+        If False, calculate the recall for the entire dataset.
 
+    Methods
+    -------
+    forward(y_true, y_pred)
+        Compute the recall metric.
+
+    Examples
+    --------
+    >>> y_true = torch.tensor([0, 1, 2, 3, 4])
+    >>> y_pred = torch.randn(5, 5).argmax(dim=-1)
+    >>> recall = Recall(num_classes=5)
+    >>> recall(y_true, y_pred)
+    0.2
+    >>> recall = Recall(num_classes=5, macro_averaging=True)
+    >>> recall(y_true, y_pred)
+    0.2
+    """
+
+    def __init__(self, num_classes, macro_averaging=False):
+        super().__init__()
         self.num_classes = num_classes
+        self.macro_averaging = macro_averaging
 
-    def forward(self, y_true, y_pred):
-        true_onehot = one_hot_encode(y_true, self.num_classes)
-        pred_onehot = one_hot_encode(y_pred, self.num_classes)
+    def forward(self, true, logits):
+        pred = logits.argmax(dim=-1)
+        y_true = one_hot_encode(true, self.num_classes)
+        y_pred = one_hot_encode(pred, self.num_classes)
 
-        true_positives = (true_onehot * pred_onehot).sum()
+        if self.macro_averaging:
+            recall = 0
+            for i in range(self.num_classes):
+                tp = (y_true[:, i] * y_pred[:, i]).sum()
+                fn = torch.sum(~y_pred[y_true[:, i].bool()].bool())
+                recall += tp / (tp + fn)
+            recall /= self.num_classes
+        else:
+            recall = self.__compute(y_true, y_pred)
 
-        false_negatives = torch.sum(~pred_onehot[true_onehot.bool()].bool())
+        return recall
 
-        recall = true_positives / (true_positives + false_negatives)
+    def __compute(self, y_true, y_pred):
+        true_positives = (y_true * y_pred).sum()
+        false_negatives = torch.sum(~y_pred[y_true.bool()].bool())
 
+        recall = true_positives / (true_positives + false_negatives)
         return recall
diff --git a/utils/models/johan_model.py b/utils/models/johan_model.py
index 8500ea9..a26e025 100644
--- a/utils/models/johan_model.py
+++ b/utils/models/johan_model.py
@@ -26,7 +26,6 @@ class JohanModel(nn.Module):
         Numer of input features.
     num_classes : int
         Number of classes in the dataset.
-
     """
 
     def __init__(self, image_shape, num_classes):
@@ -44,17 +43,15 @@ def __init__(self, image_shape, num_classes):
         self.fc3 = nn.Linear(77, 77)
         self.fc4 = nn.Linear(77, num_classes)
         self.relu = nn.ReLU()
+        self.flatten = nn.Flatten()
 
     def forward(self, x):
+        x = self.flatten(x)
         for layer in [self.fc1, self.fc2, self.fc3, self.fc4]:
             x = layer(x)
             x = self.relu(x)
         return x
 
 
-# TODO
-# Add your tests here
-
-
 if __name__ == "__main__":
-    pass  # Add your tests here
+    print("This is JohanModel")
diff --git a/utils/models/magnus_model.py b/utils/models/magnus_model.py
index c80fae0..48386ce 100644
--- a/utils/models/magnus_model.py
+++ b/utils/models/magnus_model.py
@@ -22,18 +22,18 @@ def __init__(self, image_shape: int, num_classes: int, imagechannels: int):
         self.image_shape = image_shape
         self.imagechannels = imagechannels
 
-        self.layer1 = nn.Sequential(*([
-                        nn.Linear(self.imagechannels * self.imagesize * self.imagesize, 133),
-                        nn.ReLU(),
-                    ]))
-        self.layer2 = nn.Sequential(*([
-                        nn.Linear(133, 133), 
-                        nn.ReLU()
-                    ]))
-        self.layer3 = nn.Sequential(*([
-                        nn.Linear(133, num_classes), 
-                        nn.ReLU()
-                    ]))
+        self.layer1 = nn.Sequential(
+            *(
+                [
+                    nn.Linear(
+                        self.imagechannels * self.imagesize * self.imagesize, 133
+                    ),
+                    nn.ReLU(),
+                ]
+            )
+        )
+        self.layer2 = nn.Sequential(*([nn.Linear(133, 133), nn.ReLU()]))
+        self.layer3 = nn.Sequential(*([nn.Linear(133, num_classes), nn.ReLU()]))
 
     def forward(self, x):
         """
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..6a20243
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2371 @@
+version = 1
+requires-python = ">=3.12"
+
+[[package]]
+name = "alabaster"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a6/f8/d9c74d0daf3f742840fd818d69cfae176fa332022fd44e3469487d5a9420/alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e", size = 24210 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929 },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "anyio"
+version = "4.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
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