diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..fef44948
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,33 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "semseg"
+version = "0.5.0"
+description = "SOTA Semantic Segmentation Models"
+readme = "README.md"
+license = {text = "MIT License"}
+requires-python = ">=3.10.12"
+dependencies = [
+    "coremltools>=8.1",
+    "einops~=0.8.0",
+    "fvcore~=0.1.5.post20221221",
+    "matplotlib~=3.9.2",
+    "numpy~=1.26.4",
+    "onnx>=1.17.0",
+    "rich~=13.9.4",
+    "scipy~=1.14.1",
+    "tabulate~=0.9.0",
+    "tensorboard~=2.18.0",
+    "torch>=2.5.1",
+    "torchvision>=0.20.1",
+    "tqdm~=4.67.0",
+]
+scripts = { semseg-infer = "semseg.tools.infer:main" }
+
+[project.urls]
+Repository = "https://github.com/sithu31296/semantic-segmentation"
+
+[tool.hatch.build.targets.wheel]
+packages = ["semseg"]
diff --git a/semseg/__init__.py b/semseg/__init__.py
index d072e80a..b5e18774 100644
--- a/semseg/__init__.py
+++ b/semseg/__init__.py
@@ -6,8 +6,8 @@
 
 def show_models():
     model_names = models.__all__
-    numbers = list(range(1, len(model_names)+1))
-    print(tabulate({'No.': numbers, 'Model Names': model_names}, headers='keys'))
+    numbers = list(range(1, len(model_names) + 1))
+    print(tabulate({"No.": numbers, "Model Names": model_names}, headers="keys"))
 
 
 def show_backbones():
@@ -16,18 +16,22 @@ def show_backbones():
     for name in backbone_names:
         try:
             variants.append(list(eval(f"backbones.{name.lower()}_settings").keys()))
-        except:
-            variants.append('-')
-    print(tabulate({'Backbone Names': backbone_names, 'Variants': variants}, headers='keys'))
+        except Exception:
+            variants.append("-")
+    print(
+        tabulate(
+            {"Backbone Names": backbone_names, "Variants": variants}, headers="keys"
+        )
+    )
 
 
 def show_heads():
     head_names = heads.__all__
-    numbers = list(range(1, len(head_names)+1))
-    print(tabulate({'No.': numbers, 'Heads': head_names}, headers='keys'))
+    numbers = list(range(1, len(head_names) + 1))
+    print(tabulate({"No.": numbers, "Heads": head_names}, headers="keys"))
 
 
 def show_datasets():
     dataset_names = datasets.__all__
-    numbers = list(range(1, len(dataset_names)+1))
-    print(tabulate({'No.': numbers, 'Datasets': dataset_names}, headers='keys'))
+    numbers = list(range(1, len(dataset_names) + 1))
+    print(tabulate({"No.": numbers, "Datasets": dataset_names}, headers="keys"))
diff --git a/semseg/augmentations.py b/semseg/augmentations.py
index fb75d896..05f4e819 100644
--- a/semseg/augmentations.py
+++ b/semseg/augmentations.py
@@ -1,9 +1,9 @@
-import torchvision.transforms.functional as TF 
+import torchvision.transforms.functional as TF
 import random
 import math
 import torch
 from torch import Tensor
-from typing import Tuple, List, Union, Tuple, Optional
+from typing import Tuple, List, Union
 
 
 class Compose:
@@ -23,7 +23,9 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
 
 
 class Normalize:
-    def __init__(self, mean: list = (0.485, 0.456, 0.406), std: list = (0.229, 0.224, 0.225)):
+    def __init__(
+        self, mean: list = (0.485, 0.456, 0.406), std: list = (0.229, 0.224, 0.225)
+    ):
         self.mean = mean
         self.std = std
 
@@ -136,8 +138,8 @@ def __call__(self, image, label):
 
 class Posterize:
     def __init__(self, bits=2):
-        self.bits = bits # 0-8
-        
+        self.bits = bits  # 0-8
+
     def __call__(self, image, label):
         return TF.posterize(image, self.bits), label
 
@@ -149,21 +151,43 @@ def __init__(self, angle=0, translate=[0, 0], scale=1.0, shear=[0, 0], seg_fill=
         self.scale = scale
         self.shear = shear
         self.seg_fill = seg_fill
-        
+
     def __call__(self, img, label):
-        return TF.affine(img, self.angle, self.translate, self.scale, self.shear, TF.InterpolationMode.BILINEAR, 0), TF.affine(label, self.angle, self.translate, self.scale, self.shear, TF.InterpolationMode.NEAREST, self.seg_fill) 
+        return TF.affine(
+            img,
+            self.angle,
+            self.translate,
+            self.scale,
+            self.shear,
+            TF.InterpolationMode.BILINEAR,
+            0,
+        ), TF.affine(
+            label,
+            self.angle,
+            self.translate,
+            self.scale,
+            self.shear,
+            TF.InterpolationMode.NEAREST,
+            self.seg_fill,
+        )
 
 
 class RandomRotation:
-    def __init__(self, degrees: float = 10.0, p: float = 0.2, seg_fill: int = 0, expand: bool = False) -> None:
+    def __init__(
+        self,
+        degrees: float = 10.0,
+        p: float = 0.2,
+        seg_fill: int = 0,
+        expand: bool = False,
+    ) -> None:
         """Rotate the image by a random angle between -angle and angle with probability p
 
         Args:
             p: probability
             angle: rotation angle value in degrees, counter-clockwise.
-            expand: Optional expansion flag. 
+            expand: Optional expansion flag.
                     If true, expands the output image to make it large enough to hold the entire rotated image.
-                    If false or omitted, make the output image the same size as the input image. 
+                    If false or omitted, make the output image the same size as the input image.
                     Note that the expand flag assumes rotation around the center and no translation.
         """
         self.p = p
@@ -174,10 +198,18 @@ def __init__(self, degrees: float = 10.0, p: float = 0.2, seg_fill: int = 0, exp
     def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         random_angle = random.random() * 2 * self.angle - self.angle
         if random.random() < self.p:
-            img = TF.rotate(img, random_angle, TF.InterpolationMode.BILINEAR, self.expand, fill=0)
-            mask = TF.rotate(mask, random_angle, TF.InterpolationMode.NEAREST, self.expand, fill=self.seg_fill)
+            img = TF.rotate(
+                img, random_angle, TF.InterpolationMode.BILINEAR, self.expand, fill=0
+            )
+            mask = TF.rotate(
+                mask,
+                random_angle,
+                TF.InterpolationMode.NEAREST,
+                self.expand,
+                fill=self.seg_fill,
+            )
         return img, mask
-    
+
 
 class CenterCrop:
     def __init__(self, size: Union[int, List[int], Tuple[int]]) -> None:
@@ -209,8 +241,8 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         if random.random() < self.p:
             margin_h = max(H - tH, 0)
             margin_w = max(W - tW, 0)
-            y1 = random.randint(0, margin_h+1)
-            x1 = random.randint(0, margin_w+1)
+            y1 = random.randint(0, margin_h + 1)
+            x1 = random.randint(0, margin_w + 1)
             y2 = y1 + tH
             x2 = x1 + tW
             img = img[:, y1:y2, x1:x2]
@@ -219,7 +251,9 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
 
 
 class Pad:
-    def __init__(self, size: Union[List[int], Tuple[int], int], seg_fill: int = 0) -> None:
+    def __init__(
+        self, size: Union[List[int], Tuple[int], int], seg_fill: int = 0
+    ) -> None:
         """Pad the given image on all sides with the given "pad" value.
         Args:
             size: expected output image size (h, w)
@@ -229,16 +263,18 @@ def __init__(self, size: Union[List[int], Tuple[int], int], seg_fill: int = 0) -
         self.seg_fill = seg_fill
 
     def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
-        padding = (0, 0, self.size[1]-img.shape[2], self.size[0]-img.shape[1])
+        padding = (0, 0, self.size[1] - img.shape[2], self.size[0] - img.shape[1])
         return TF.pad(img, padding), TF.pad(mask, padding, self.seg_fill)
 
 
 class ResizePad:
-    def __init__(self, size: Union[int, Tuple[int], List[int]], seg_fill: int = 0) -> None:
+    def __init__(
+        self, size: Union[int, Tuple[int], List[int]], seg_fill: int = 0
+    ) -> None:
         """Resize the input image to the given size.
         Args:
-            size: Desired output size. 
-                If size is a sequence, the output size will be matched to this. 
+            size: Desired output size.
+                If size is a sequence, the output size will be matched to this.
                 If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio.
         """
         self.size = size
@@ -248,10 +284,10 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         H, W = img.shape[1:]
         tH, tW = self.size
 
-        # scale the image 
-        scale_factor = min(tH/H, tW/W) if W > H else max(tH/H, tW/W)
+        # scale the image
+        scale_factor = min(tH / H, tW / W) if W > H else max(tH / H, tW / W)
         # nH, nW = int(H * scale_factor + 0.5), int(W * scale_factor + 0.5)
-        nH, nW = round(H*scale_factor), round(W*scale_factor)
+        nH, nW = round(H * scale_factor), round(W * scale_factor)
         img = TF.resize(img, (nH, nW), TF.InterpolationMode.BILINEAR)
         mask = TF.resize(mask, (nH, nW), TF.InterpolationMode.NEAREST)
 
@@ -259,15 +295,15 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         padding = [0, 0, tW - nW, tH - nH]
         img = TF.pad(img, padding, fill=0)
         mask = TF.pad(mask, padding, fill=self.seg_fill)
-        return img, mask 
+        return img, mask
 
 
 class Resize:
     def __init__(self, size: Union[int, Tuple[int], List[int]]) -> None:
         """Resize the input image to the given size.
         Args:
-            size: Desired output size. 
-                If size is a sequence, the output size will be matched to this. 
+            size: Desired output size.
+                If size is a sequence, the output size will be matched to this.
                 If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio.
         """
         self.size = size
@@ -275,9 +311,9 @@ def __init__(self, size: Union[int, Tuple[int], List[int]]) -> None:
     def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         H, W = img.shape[1:]
 
-        # scale the image 
+        # scale the image
         scale_factor = self.size[0] / min(H, W)
-        nH, nW = round(H*scale_factor), round(W*scale_factor)
+        nH, nW = round(H * scale_factor), round(W * scale_factor)
         img = TF.resize(img, (nH, nW), TF.InterpolationMode.BILINEAR)
         mask = TF.resize(mask, (nH, nW), TF.InterpolationMode.NEAREST)
 
@@ -285,13 +321,17 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         alignH, alignW = int(math.ceil(nH / 32)) * 32, int(math.ceil(nW / 32)) * 32
         img = TF.resize(img, (alignH, alignW), TF.InterpolationMode.BILINEAR)
         mask = TF.resize(mask, (alignH, alignW), TF.InterpolationMode.NEAREST)
-        return img, mask 
+        return img, mask
 
 
 class RandomResizedCrop:
-    def __init__(self, size: Union[int, Tuple[int], List[int]], scale: Tuple[float, float] = (0.5, 2.0), seg_fill: int = 0) -> None:
-        """Resize the input image to the given size.
-        """
+    def __init__(
+        self,
+        size: Union[int, Tuple[int], List[int]],
+        scale: Tuple[float, float] = (0.5, 2.0),
+        seg_fill: int = 0,
+    ) -> None:
+        """Resize the input image to the given size."""
         self.size = size
         self.scale = scale
         self.seg_fill = seg_fill
@@ -303,10 +343,10 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         # get the scale
         ratio = random.random() * (self.scale[1] - self.scale[0]) + self.scale[0]
         # ratio = random.uniform(min(self.scale), max(self.scale))
-        scale = int(tH*ratio), int(tW*4*ratio)
+        scale = int(tH * ratio), int(tW * 4 * ratio)
 
-        # scale the image 
-        scale_factor = min(max(scale)/max(H, W), min(scale)/min(H, W))
+        # scale the image
+        scale_factor = min(max(scale) / max(H, W), min(scale) / min(H, W))
         nH, nW = int(H * scale_factor + 0.5), int(W * scale_factor + 0.5)
         # nH, nW = int(math.ceil(nH / 32)) * 32, int(math.ceil(nW / 32)) * 32
         img = TF.resize(img, (nH, nW), TF.InterpolationMode.BILINEAR)
@@ -315,8 +355,8 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
         # random crop
         margin_h = max(img.shape[1] - tH, 0)
         margin_w = max(img.shape[2] - tW, 0)
-        y1 = random.randint(0, margin_h+1)
-        x1 = random.randint(0, margin_w+1)
+        y1 = random.randint(0, margin_h + 1)
+        x1 = random.randint(0, margin_w + 1)
         y2 = y1 + tH
         x2 = x1 + tW
         img = img[:, y1:y2, x1:x2]
@@ -327,40 +367,43 @@ def __call__(self, img: Tensor, mask: Tensor) -> Tuple[Tensor, Tensor]:
             padding = [0, 0, tW - img.shape[2], tH - img.shape[1]]
             img = TF.pad(img, padding, fill=0)
             mask = TF.pad(mask, padding, fill=self.seg_fill)
-        return img, mask 
-
+        return img, mask
 
 
 def get_train_augmentation(size: Union[int, Tuple[int], List[int]], seg_fill: int = 0):
-    return Compose([
-        # ColorJitter(brightness=0.0, contrast=0.5, saturation=0.5, hue=0.5),
-        # RandomAdjustSharpness(sharpness_factor=0.1, p=0.5),
-        # RandomAutoContrast(p=0.2),
-        RandomHorizontalFlip(p=0.5),
-        # RandomVerticalFlip(p=0.5),
-        # RandomGaussianBlur((3, 3), p=0.5),
-        # RandomGrayscale(p=0.5),
-        # RandomRotation(degrees=10, p=0.3, seg_fill=seg_fill),
-        RandomResizedCrop(size, scale=(0.5, 2.0), seg_fill=seg_fill),
-        Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-    ])
+    return Compose(
+        [
+            # ColorJitter(brightness=0.0, contrast=0.5, saturation=0.5, hue=0.5),
+            # RandomAdjustSharpness(sharpness_factor=0.1, p=0.5),
+            # RandomAutoContrast(p=0.2),
+            RandomHorizontalFlip(p=0.5),
+            # RandomVerticalFlip(p=0.5),
+            # RandomGaussianBlur((3, 3), p=0.5),
+            # RandomGrayscale(p=0.5),
+            # RandomRotation(degrees=10, p=0.3, seg_fill=seg_fill),
+            RandomResizedCrop(size, scale=(0.5, 2.0), seg_fill=seg_fill),
+            Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        ]
+    )
+
 
 def get_val_augmentation(size: Union[int, Tuple[int], List[int]]):
-    return Compose([
-        Resize(size),
-        Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-    ])
+    return Compose(
+        [Resize(size), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]
+    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     h = 230
     w = 420
     img = torch.randn(3, h, w)
     mask = torch.randn(1, h, w)
-    aug = Compose([
-        RandomResizedCrop((512, 512)),
-        # RandomCrop((512, 512), p=1.0),
-        # Pad((512, 512))
-    ])
+    aug = Compose(
+        [
+            RandomResizedCrop((512, 512)),
+            # RandomCrop((512, 512), p=1.0),
+            # Pad((512, 512))
+        ]
+    )
     img, mask = aug(img, mask)
-    print(img.shape, mask.shape)
\ No newline at end of file
+    print(img.shape, mask.shape)
diff --git a/semseg/schedulers.py b/semseg/schedulers.py
index 4bbb9432..ea05a576 100644
--- a/semseg/schedulers.py
+++ b/semseg/schedulers.py
@@ -4,7 +4,9 @@
 
 
 class PolyLR(_LRScheduler):
-    def __init__(self, optimizer, max_iter, decay_iter=1, power=0.9, last_epoch=-1) -> None:
+    def __init__(
+        self, optimizer, max_iter, decay_iter=1, power=0.9, last_epoch=-1
+    ) -> None:
         self.decay_iter = decay_iter
         self.max_iter = max_iter
         self.power = power
@@ -15,11 +17,13 @@ def get_lr(self):
             return self.base_lrs
         else:
             factor = (1 - self.last_epoch / float(self.max_iter)) ** self.power
-            return [factor*lr for lr in self.base_lrs]
+            return [factor * lr for lr in self.base_lrs]
 
 
 class WarmupLR(_LRScheduler):
-    def __init__(self, optimizer, warmup_iter=500, warmup_ratio=5e-4, warmup='exp', last_epoch=-1) -> None:
+    def __init__(
+        self, optimizer, warmup_iter=500, warmup_ratio=5e-4, warmup="exp", last_epoch=-1
+    ) -> None:
         self.warmup_iter = warmup_iter
         self.warmup_ratio = warmup_ratio
         self.warmup = warmup
@@ -30,20 +34,37 @@ def get_lr(self):
         return [ratio * lr for lr in self.base_lrs]
 
     def get_lr_ratio(self):
-        return self.get_warmup_ratio() if self.last_epoch < self.warmup_iter else self.get_main_ratio()
+        return (
+            self.get_warmup_ratio()
+            if self.last_epoch < self.warmup_iter
+            else self.get_main_ratio()
+        )
 
     def get_main_ratio(self):
         raise NotImplementedError
 
     def get_warmup_ratio(self):
-        assert self.warmup in ['linear', 'exp']
+        assert self.warmup in ["linear", "exp"]
         alpha = self.last_epoch / self.warmup_iter
 
-        return self.warmup_ratio + (1. - self.warmup_ratio) * alpha if self.warmup == 'linear' else self.warmup_ratio ** (1. - alpha)
+        return (
+            self.warmup_ratio + (1.0 - self.warmup_ratio) * alpha
+            if self.warmup == "linear"
+            else self.warmup_ratio ** (1.0 - alpha)
+        )
 
 
 class WarmupPolyLR(WarmupLR):
-    def __init__(self, optimizer, power, max_iter, warmup_iter=500, warmup_ratio=5e-4, warmup='exp', last_epoch=-1) -> None:
+    def __init__(
+        self,
+        optimizer,
+        power,
+        max_iter,
+        warmup_iter=500,
+        warmup_ratio=5e-4,
+        warmup="exp",
+        last_epoch=-1,
+    ) -> None:
         self.power = power
         self.max_iter = max_iter
         super().__init__(optimizer, warmup_iter, warmup_ratio, warmup, last_epoch)
@@ -57,7 +78,16 @@ def get_main_ratio(self):
 
 
 class WarmupExpLR(WarmupLR):
-    def __init__(self, optimizer, gamma, interval=1, warmup_iter=500, warmup_ratio=5e-4, warmup='exp', last_epoch=-1) -> None:
+    def __init__(
+        self,
+        optimizer,
+        gamma,
+        interval=1,
+        warmup_iter=500,
+        warmup_ratio=5e-4,
+        warmup="exp",
+        last_epoch=-1,
+    ) -> None:
         self.gamma = gamma
         self.interval = interval
         super().__init__(optimizer, warmup_iter, warmup_ratio, warmup, last_epoch)
@@ -68,37 +98,70 @@ def get_main_ratio(self):
 
 
 class WarmupCosineLR(WarmupLR):
-    def __init__(self, optimizer, max_iter, eta_ratio=0, warmup_iter=500, warmup_ratio=5e-4, warmup='exp', last_epoch=-1) -> None:
+    def __init__(
+        self,
+        optimizer,
+        max_iter,
+        eta_ratio=0,
+        warmup_iter=500,
+        warmup_ratio=5e-4,
+        warmup="exp",
+        last_epoch=-1,
+    ) -> None:
         self.eta_ratio = eta_ratio
         self.max_iter = max_iter
         super().__init__(optimizer, warmup_iter, warmup_ratio, warmup, last_epoch)
 
     def get_main_ratio(self):
-        real_iter = self.last_epoch - self.warmup_iter
         real_max_iter = self.max_iter - self.warmup_iter
-        
-        return self.eta_ratio + (1 - self.eta_ratio) * (1 + math.cos(math.pi * self.last_epoch / real_max_iter)) / 2
-
-
-
-__all__ = ['polylr', 'warmuppolylr', 'warmupcosinelr', 'warmupsteplr']
-
 
-def get_scheduler(scheduler_name: str, optimizer, max_iter: int, power: int, warmup_iter: int, warmup_ratio: float):
-    assert scheduler_name in __all__, f"Unavailable scheduler name >> {scheduler_name}.\nAvailable schedulers: {__all__}"
-    if scheduler_name == 'warmuppolylr':
-        return WarmupPolyLR(optimizer, power, max_iter, warmup_iter, warmup_ratio, warmup='linear')
-    elif scheduler_name == 'warmupcosinelr':
-        return WarmupCosineLR(optimizer, max_iter, warmup_iter=warmup_iter, warmup_ratio=warmup_ratio)
+        return (
+            self.eta_ratio
+            + (1 - self.eta_ratio)
+            * (1 + math.cos(math.pi * self.last_epoch / real_max_iter))
+            / 2
+        )
+
+
+__all__ = ["PolyLR", "WarmupPolyLR", "WarmupCosineLR", "WarmupExpLR"]
+
+
+def get_scheduler(
+    scheduler_name: str,
+    optimizer,
+    max_iter: int,
+    power: int,
+    warmup_iter: int,
+    warmup_ratio: float,
+):
+    assert (
+        scheduler_name in __all__
+    ), f"Unavailable scheduler name >> {scheduler_name}.\nAvailable schedulers: {__all__}"
+    if scheduler_name == "warmuppolylr":
+        return WarmupPolyLR(
+            optimizer, power, max_iter, warmup_iter, warmup_ratio, warmup="linear"
+        )
+    elif scheduler_name == "warmupcosinelr":
+        return WarmupCosineLR(
+            optimizer, max_iter, warmup_iter=warmup_iter, warmup_ratio=warmup_ratio
+        )
     return PolyLR(optimizer, max_iter)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     model = torch.nn.Conv2d(3, 16, 3, 1, 1)
     optim = torch.optim.SGD(model.parameters(), lr=1e-3)
 
     max_iter = 20000
-    sched = WarmupPolyLR(optim, power=0.9, max_iter=max_iter, warmup_iter=200, warmup_ratio=0.1, warmup='exp', last_epoch=-1)
+    sched = WarmupPolyLR(
+        optim,
+        power=0.9,
+        max_iter=max_iter,
+        warmup_iter=200,
+        warmup_ratio=0.1,
+        warmup="exp",
+        last_epoch=-1,
+    )
 
     lrs = []
 
@@ -109,8 +172,8 @@ def get_scheduler(scheduler_name: str, optimizer, max_iter: int, power: int, war
         sched.step()
 
     import matplotlib.pyplot as plt
-    import numpy as np 
+    import numpy as np
 
     plt.plot(np.arange(len(lrs)), np.array(lrs))
     plt.grid()
-    plt.show()
\ No newline at end of file
+    plt.show()
diff --git a/semseg/tools/__init__.py b/semseg/tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/semseg/tools/benchmark.py b/semseg/tools/benchmark.py
new file mode 100644
index 00000000..98fa46b9
--- /dev/null
+++ b/semseg/tools/benchmark.py
@@ -0,0 +1,56 @@
+import time
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+
+import torch
+from fvcore.nn import FlopCountAnalysis, flop_count_table
+
+from semseg.models import *  # noqa: F403
+
+
+def main(
+    model_name: str,
+    backbone_name: str,
+    image_size: list,
+    num_classes: int,
+    device: str,
+):
+    device = torch.device(
+        "cuda" if torch.cuda.is_available() and device == "cuda" else "cpu"
+    )
+    inputs = torch.randn(1, 3, *image_size).to(device)
+    model = eval(model_name)(backbone_name, num_classes)
+    model = model.to(device)
+    model.eval()
+
+    print(flop_count_table(FlopCountAnalysis(model, inputs)))
+
+    total_time = 0.0
+    for _ in range(10):
+        tic = time.perf_counter()
+        model(inputs)
+        toc = time.perf_counter()
+        total_time += toc - tic
+    total_time /= 10
+    print(f"Inference time: {total_time*1000:.2f}ms")
+    print(f"FPS: {1/total_time}")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        formatter_class=ArgumentDefaultsHelpFormatter,
+        description="Benchmarking the model",
+    )
+    parser.add_argument("--model-name", type=str, default="SegFormer")
+    parser.add_argument("--backbone-name", type=str, default="MiT-B0")
+    parser.add_argument("--image-size", type=list, default=[512, 512])
+    parser.add_argument("--num-classes", type=int, default=11)
+    parser.add_argument("--device", type=str, default="cuda")
+    args = parser.parse_args()
+
+    main(
+        args.model_name,
+        args.backbone_name,
+        args.image_size,
+        args.num_classes,
+        args.device,
+    )
diff --git a/semseg/tools/export.py b/semseg/tools/export.py
new file mode 100644
index 00000000..70da71df
--- /dev/null
+++ b/semseg/tools/export.py
@@ -0,0 +1,73 @@
+import argparse
+from pathlib import Path
+
+import onnx
+import torch
+import yaml
+
+from semseg.datasets import *  # noqa: F403
+from semseg.models import *  # noqa: F403
+
+
+def export_onnx(model, inputs, file):
+    torch.onnx.export(
+        model,
+        inputs,
+        f"{cfg['TEST']['MODEL_PATH'].split('.')[0]}.onnx",
+        input_names=["input"],
+        output_names=["output"],
+        opset_version=13,
+    )
+    onnx_model = onnx.load(f"{file}.onnx")
+    onnx.checker.check_model(onnx_model)
+    print(f"ONNX model saved to {file}.onnx")
+
+
+def export_coreml(model, inputs, file):
+    try:
+        import coremltools as ct
+
+        ts_model = torch.jit.trace(model, inputs, strict=True)
+        ct_model = ct.convert(
+            ts_model,
+            inputs=[
+                ct.ImageType(
+                    "image", shape=inputs.shape, scale=1 / 255.0, bias=[0, 0, 0]
+                )
+            ],
+        )
+        ct_model.save(f"{file}.mlmodel")
+        print(f"CoreML model saved to {file}.mlmodel")
+    except Exception:
+        print(
+            "Please install coremltools to export to CoreML.\n`pip install coremltools`"
+        )
+
+
+def main(cfg):
+    model = eval(cfg["MODEL"]["NAME"])(
+        cfg["MODEL"]["BACKBONE"], len(eval(cfg["DATASET"]["NAME"]).PALETTE)
+    )
+    model.load_state_dict(torch.load(cfg["TEST"]["MODEL_PATH"], map_location="cpu"))
+    model.eval()
+
+    inputs = torch.randn(1, 3, *cfg["TEST"]["IMAGE_SIZE"])
+    file = cfg["TEST"]["MODEL_PATH"].split(".")[0]
+
+    export_onnx(model, inputs, file)
+    export_coreml(model, inputs, file)
+    print("Finished converting.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cfg", type=str, default="configs/custom.yaml")
+    args = parser.parse_args()
+
+    with open(args.cfg) as f:
+        cfg = yaml.load(f, Loader=yaml.SafeLoader)
+
+    save_dir = Path(cfg["SAVE_DIR"])
+    save_dir.mkdir(exist_ok=True)
+
+    main(cfg)
diff --git a/tools/infer.py b/semseg/tools/infer.py
similarity index 60%
rename from tools/infer.py
rename to semseg/tools/infer.py
index 4c89705a..8be438b9 100644
--- a/tools/infer.py
+++ b/semseg/tools/infer.py
@@ -1,50 +1,58 @@
+import math
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+from pathlib import Path
+
 import torch
-import argparse
 import yaml
-import math
+from rich.console import Console
 from torch import Tensor
 from torch.nn import functional as F
-from pathlib import Path
 from torchvision import io
 from torchvision import transforms as T
-from semseg.models import *
-from semseg.datasets import *
+
+from semseg.datasets import *  # noqa: F403
+from semseg.models import *  # noqa: F403
 from semseg.utils.utils import timer
 from semseg.utils.visualize import draw_text
 
-from rich.console import Console
 console = Console()
 
 
 class SemSeg:
     def __init__(self, cfg) -> None:
         # inference device cuda or cpu
-        self.device = torch.device(cfg['DEVICE'])
+        self.device = torch.device(cfg["DEVICE"])
 
         # get dataset classes' colors and labels
-        self.palette = eval(cfg['DATASET']['NAME']).PALETTE
-        self.labels = eval(cfg['DATASET']['NAME']).CLASSES
+        self.palette = eval(cfg["DATASET"]["NAME"]).PALETTE
+        self.labels = eval(cfg["DATASET"]["NAME"]).CLASSES
 
         # initialize the model and load weights and send to device
-        self.model = eval(cfg['MODEL']['NAME'])(cfg['MODEL']['BACKBONE'], len(self.palette))
-        self.model.load_state_dict(torch.load(cfg['TEST']['MODEL_PATH'], map_location='cpu'))
+        self.model = eval(cfg["MODEL"]["NAME"])(
+            cfg["MODEL"]["BACKBONE"], len(self.palette)
+        )
+        self.model.load_state_dict(
+            torch.load(cfg["TEST"]["MODEL_PATH"], map_location="cpu")
+        )
         self.model = self.model.to(self.device)
         self.model.eval()
 
         # preprocess parameters and transformation pipeline
-        self.size = cfg['TEST']['IMAGE_SIZE']
-        self.tf_pipeline = T.Compose([
-            T.Lambda(lambda x: x / 255),
-            T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
-            T.Lambda(lambda x: x.unsqueeze(0))
-        ])
+        self.size = cfg["TEST"]["IMAGE_SIZE"]
+        self.tf_pipeline = T.Compose(
+            [
+                T.Lambda(lambda x: x / 255),
+                T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+                T.Lambda(lambda x: x.unsqueeze(0)),
+            ]
+        )
 
     def preprocess(self, image: Tensor) -> Tensor:
         H, W = image.shape[1:]
         console.print(f"Original Image Size > [red]{H}x{W}[/red]")
         # scale the short side of image to target size
         scale_factor = self.size[0] / min(H, W)
-        nH, nW = round(H*scale_factor), round(W*scale_factor)
+        nH, nW = round(H * scale_factor), round(W * scale_factor)
         # make it divisible by model stride
         nH, nW = int(math.ceil(nH / 32)) * 32, int(math.ceil(nW / 32)) * 32
         console.print(f"Inference Image Size > [red]{nH}x{nW}[/red]")
@@ -56,13 +64,15 @@ def preprocess(self, image: Tensor) -> Tensor:
 
     def postprocess(self, orig_img: Tensor, seg_map: Tensor, overlay: bool) -> Tensor:
         # resize to original image size
-        seg_map = F.interpolate(seg_map, size=orig_img.shape[-2:], mode='bilinear', align_corners=True)
+        seg_map = F.interpolate(
+            seg_map, size=orig_img.shape[-2:], mode="bilinear", align_corners=True
+        )
         # get segmentation map (value being 0 to num_classes)
         seg_map = seg_map.softmax(dim=1).argmax(dim=1).cpu().to(int)
 
         # convert segmentation map to color map
         seg_image = self.palette[seg_map].squeeze()
-        if overlay: 
+        if overlay:
             seg_image = (orig_img.permute(1, 2, 0) * 0.4) + (seg_image * 0.6)
 
         image = draw_text(seg_image, seg_map, self.labels)
@@ -72,7 +82,7 @@ def postprocess(self, orig_img: Tensor, seg_map: Tensor, overlay: bool) -> Tenso
     @timer
     def model_forward(self, img: Tensor) -> Tensor:
         return self.model(img)
-        
+
     def predict(self, img_fname: str, overlay: bool) -> Tensor:
         image = io.read_image(img_fname)
         img = self.preprocess(image)
@@ -81,36 +91,45 @@ def predict(self, img_fname: str, overlay: bool) -> Tensor:
         return seg_map
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='configs/ade20k.yaml')
+def main():
+    parser = ArgumentParser(
+        formatter_class=ArgumentDefaultsHelpFormatter,
+        description="Inference script for semantic segmentation",
+    )
+    parser.add_argument("--cfg", type=str, default="configs/ade20k.yaml")
     args = parser.parse_args()
 
     with open(args.cfg) as f:
         cfg = yaml.load(f, Loader=yaml.SafeLoader)
 
-    test_file = Path(cfg['TEST']['FILE'])
+    test_file = Path(cfg["TEST"]["FILE"])
     if not test_file.exists():
         raise FileNotFoundError(test_file)
 
-    console.print(f"Model > [red]{cfg['MODEL']['NAME']} {cfg['MODEL']['BACKBONE']}[/red]")
+    console.print(
+        f"Model > [red]{cfg['MODEL']['NAME']} {cfg['MODEL']['BACKBONE']}[/red]"
+    )
     console.print(f"Model > [red]{cfg['DATASET']['NAME']}[/red]")
 
-    save_dir = Path(cfg['SAVE_DIR']) / 'test_results'
+    save_dir = Path(cfg["SAVE_DIR"]) / "test_results"
     save_dir.mkdir(exist_ok=True)
-    
+
     semseg = SemSeg(cfg)
 
     with console.status("[bright_green]Processing..."):
         if test_file.is_file():
-            console.rule(f'[green]{test_file}')
-            segmap = semseg.predict(str(test_file), cfg['TEST']['OVERLAY'])
+            console.rule(f"[green]{test_file}")
+            segmap = semseg.predict(str(test_file), cfg["TEST"]["OVERLAY"])
             segmap.save(save_dir / f"{str(test_file.stem)}.png")
         else:
-            files = test_file.glob('*.*')
+            files = test_file.glob("*.*")
             for file in files:
-                console.rule(f'[green]{file}')
-                segmap = semseg.predict(str(file), cfg['TEST']['OVERLAY'])
+                console.rule(f"[green]{file}")
+                segmap = semseg.predict(str(file), cfg["TEST"]["OVERLAY"])
                 segmap.save(save_dir / f"{str(file.stem)}.png")
 
-    console.rule(f"[cyan]Segmentation results are saved in `{save_dir}`")
\ No newline at end of file
+    console.rule(f"[cyan]Segmentation results are saved in `{save_dir}`")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/semseg/tools/train.py b/semseg/tools/train.py
new file mode 100644
index 00000000..e9c7f4f3
--- /dev/null
+++ b/semseg/tools/train.py
@@ -0,0 +1,169 @@
+import multiprocessing as mp
+import time
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+from pathlib import Path
+
+import torch
+import yaml
+from tabulate import tabulate
+from torch import distributed as dist
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from semseg.augmentations import get_train_augmentation, get_val_augmentation
+from semseg.datasets import *  # noqa: F403
+from semseg.losses import get_loss
+from semseg.models import *  # noqa: F403
+from semseg.optimizers import get_optimizer
+from semseg.schedulers import get_scheduler
+from semseg.tools.val import evaluate
+from semseg.utils.utils import cleanup_ddp, fix_seeds, setup_cudnn, setup_ddp
+
+
+def main(cfg, gpu, save_dir):
+    start = time.time()
+    best_mIoU = 0.0
+    num_workers = mp.cpu_count()
+    device = torch.device(cfg["DEVICE"])
+    train_cfg, eval_cfg = cfg["TRAIN"], cfg["EVAL"]
+    dataset_cfg, model_cfg = cfg["DATASET"], cfg["MODEL"]
+    loss_cfg, optim_cfg, sched_cfg = cfg["LOSS"], cfg["OPTIMIZER"], cfg["SCHEDULER"]
+    epochs, lr = train_cfg["EPOCHS"], optim_cfg["LR"]
+
+    traintransform = get_train_augmentation(
+        train_cfg["IMAGE_SIZE"], seg_fill=dataset_cfg["IGNORE_LABEL"]
+    )
+    valtransform = get_val_augmentation(eval_cfg["IMAGE_SIZE"])
+
+    trainset = eval(dataset_cfg["NAME"])(dataset_cfg["ROOT"], "train", traintransform)
+    valset = eval(dataset_cfg["NAME"])(dataset_cfg["ROOT"], "val", valtransform)
+
+    model = eval(model_cfg["NAME"])(model_cfg["BACKBONE"], trainset.n_classes)
+    model.init_pretrained(model_cfg["PRETRAINED"])
+    model = model.to(device)
+
+    if train_cfg["DDP"]:
+        sampler = DistributedSampler(
+            trainset, dist.get_world_size(), dist.get_rank(), shuffle=True
+        )
+        model = DDP(model, device_ids=[gpu])
+    else:
+        sampler = RandomSampler(trainset)
+
+    trainloader = DataLoader(
+        trainset,
+        batch_size=train_cfg["BATCH_SIZE"],
+        num_workers=num_workers,
+        drop_last=True,
+        pin_memory=True,
+        sampler=sampler,
+    )
+    valloader = DataLoader(valset, batch_size=1, num_workers=1, pin_memory=True)
+
+    iters_per_epoch = len(trainset) // train_cfg["BATCH_SIZE"]
+    # class_weights = trainset.class_weights.to(device)
+    loss_fn = get_loss(loss_cfg["NAME"], trainset.ignore_label, None)
+    optimizer = get_optimizer(model, optim_cfg["NAME"], lr, optim_cfg["WEIGHT_DECAY"])
+    scheduler = get_scheduler(
+        sched_cfg["NAME"],
+        optimizer,
+        epochs * iters_per_epoch,
+        sched_cfg["POWER"],
+        iters_per_epoch * sched_cfg["WARMUP"],
+        sched_cfg["WARMUP_RATIO"],
+    )
+    scaler = GradScaler(enabled=train_cfg["AMP"])
+    writer = SummaryWriter(str(save_dir / "logs"))
+
+    for epoch in range(epochs):
+        model.train()
+        if train_cfg["DDP"]:
+            sampler.set_epoch(epoch)
+
+        train_loss = 0.0
+        pbar = tqdm(
+            enumerate(trainloader),
+            total=iters_per_epoch,
+            desc=f"Epoch: [{epoch+1}/{epochs}] Iter: [{0}/{iters_per_epoch}] LR: {lr:.8f} Loss: {train_loss:.8f}",
+        )
+
+        for iter, (img, lbl) in pbar:
+            optimizer.zero_grad(set_to_none=True)
+
+            img = img.to(device)
+            lbl = lbl.to(device)
+
+            with autocast(enabled=train_cfg["AMP"]):
+                logits = model(img)
+                loss = loss_fn(logits, lbl)
+
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+            torch.cuda.synchronize()
+
+            lr = scheduler.get_lr()
+            lr = sum(lr) / len(lr)
+            train_loss += loss.item()
+
+            pbar.set_description(
+                f"Epoch: [{epoch+1}/{epochs}] Iter: [{iter+1}/{iters_per_epoch}] LR: {lr:.8f} Loss: {train_loss / (iter+1):.8f}"
+            )
+
+        train_loss /= iter + 1
+        writer.add_scalar("train/loss", train_loss, epoch)
+        torch.cuda.empty_cache()
+
+        if (epoch + 1) % train_cfg["EVAL_INTERVAL"] == 0 or (epoch + 1) == epochs:
+            miou = evaluate(model, valloader, device)[-1]
+            writer.add_scalar("val/mIoU", miou, epoch)
+
+            if miou > best_mIoU:
+                best_mIoU = miou
+                torch.save(
+                    model.module.state_dict()
+                    if train_cfg["DDP"]
+                    else model.state_dict(),
+                    save_dir
+                    / f"{model_cfg['NAME']}_{model_cfg['BACKBONE']}_{dataset_cfg['NAME']}.pth",
+                )
+            print(f"Current mIoU: {miou} Best mIoU: {best_mIoU}")
+
+    writer.close()
+    pbar.close()
+    end = time.gmtime(time.time() - start)
+
+    table = [
+        ["Best mIoU", f"{best_mIoU:.2f}"],
+        ["Total Training Time", time.strftime("%H:%M:%S", end)],
+    ]
+    print(tabulate(table, numalign="right"))
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        formatter_class=ArgumentDefaultsHelpFormatter,
+        description="Training script for semantic segmentation",
+    )
+    parser.add_argument(
+        "--cfg",
+        type=str,
+        default="configs/custom.yaml",
+        help="Configuration file to use",
+    )
+    args = parser.parse_args()
+
+    with open(args.cfg) as f:
+        cfg = yaml.load(f, Loader=yaml.SafeLoader)
+
+    fix_seeds(3407)
+    setup_cudnn()
+    gpu = setup_ddp()
+    save_dir = Path(cfg["SAVE_DIR"])
+    save_dir.mkdir(exist_ok=True)
+    main(cfg, gpu, save_dir)
+    cleanup_ddp()
diff --git a/tools/val.py b/semseg/tools/val.py
similarity index 54%
rename from tools/val.py
rename to semseg/tools/val.py
index 950fa15e..9c6a363a 100644
--- a/tools/val.py
+++ b/semseg/tools/val.py
@@ -1,35 +1,39 @@
-import torch
-import argparse
-import yaml
 import math
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
 from pathlib import Path
-from tqdm import tqdm
+
+import torch
+import yaml
 from tabulate import tabulate
-from torch.utils.data import DataLoader
 from torch.nn import functional as F
-from semseg.models import *
-from semseg.datasets import *
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
 from semseg.augmentations import get_val_augmentation
+from semseg.datasets import *  # noqa: F403
 from semseg.metrics import Metrics
+from semseg.models import *  # noqa: F403
 from semseg.utils.utils import setup_cudnn
 
 
 @torch.no_grad()
 def evaluate(model, dataloader, device):
-    print('Evaluating...')
+    print("Evaluating...")
     model.eval()
-    metrics = Metrics(dataloader.dataset.n_classes, dataloader.dataset.ignore_label, device)
+    metrics = Metrics(
+        dataloader.dataset.n_classes, dataloader.dataset.ignore_label, device
+    )
 
     for images, labels in tqdm(dataloader):
         images = images.to(device)
         labels = labels.to(device)
         preds = model(images).softmax(dim=1)
         metrics.update(preds, labels)
-    
+
     ious, miou = metrics.compute_iou()
     acc, macc = metrics.compute_pixel_acc()
     f1, mf1 = metrics.compute_f1()
-    
+
     return acc, macc, f1, mf1, ious, miou
 
 
@@ -47,22 +51,31 @@ def evaluate_msf(model, dataloader, device, scales, flip):
 
         for scale in scales:
             new_H, new_W = int(scale * H), int(scale * W)
-            new_H, new_W = int(math.ceil(new_H / 32)) * 32, int(math.ceil(new_W / 32)) * 32
-            scaled_images = F.interpolate(images, size=(new_H, new_W), mode='bilinear', align_corners=True)
+            new_H, new_W = (
+                int(math.ceil(new_H / 32)) * 32,
+                int(math.ceil(new_W / 32)) * 32,
+            )
+            scaled_images = F.interpolate(
+                images, size=(new_H, new_W), mode="bilinear", align_corners=True
+            )
             scaled_images = scaled_images.to(device)
             logits = model(scaled_images)
-            logits = F.interpolate(logits, size=(H, W), mode='bilinear', align_corners=True)
+            logits = F.interpolate(
+                logits, size=(H, W), mode="bilinear", align_corners=True
+            )
             scaled_logits += logits.softmax(dim=1)
 
             if flip:
                 scaled_images = torch.flip(scaled_images, dims=(3,))
                 logits = model(scaled_images)
                 logits = torch.flip(logits, dims=(3,))
-                logits = F.interpolate(logits, size=(H, W), mode='bilinear', align_corners=True)
+                logits = F.interpolate(
+                    logits, size=(H, W), mode="bilinear", align_corners=True
+                )
                 scaled_logits += logits.softmax(dim=1)
 
         metrics.update(scaled_logits, labels)
-    
+
     acc, macc = metrics.compute_pixel_acc()
     f1, mf1 = metrics.compute_f1()
     ious, miou = metrics.compute_iou()
@@ -70,43 +83,56 @@ def evaluate_msf(model, dataloader, device, scales, flip):
 
 
 def main(cfg):
-    device = torch.device(cfg['DEVICE'])
+    device = torch.device(cfg["DEVICE"])
 
-    eval_cfg = cfg['EVAL']
-    transform = get_val_augmentation(eval_cfg['IMAGE_SIZE'])
-    dataset = eval(cfg['DATASET']['NAME'])(cfg['DATASET']['ROOT'], 'val', transform)
+    eval_cfg = cfg["EVAL"]
+    transform = get_val_augmentation(eval_cfg["IMAGE_SIZE"])
+    dataset = eval(cfg["DATASET"]["NAME"])(cfg["DATASET"]["ROOT"], "val", transform)
     dataloader = DataLoader(dataset, 1, num_workers=1, pin_memory=True)
 
-    model_path = Path(eval_cfg['MODEL_PATH'])
-    if not model_path.exists(): model_path = Path(cfg['SAVE_DIR']) / f"{cfg['MODEL']['NAME']}_{cfg['MODEL']['BACKBONE']}_{cfg['DATASET']['NAME']}.pth"
+    model_path = Path(eval_cfg["MODEL_PATH"])
+    if not model_path.exists():
+        model_path = (
+            Path(cfg["SAVE_DIR"])
+            / f"{cfg['MODEL']['NAME']}_{cfg['MODEL']['BACKBONE']}_{cfg['DATASET']['NAME']}.pth"
+        )
     print(f"Evaluating {model_path}...")
 
-    model = eval(cfg['MODEL']['NAME'])(cfg['MODEL']['BACKBONE'], dataset.n_classes)
-    model.load_state_dict(torch.load(str(model_path), map_location='cpu'))
+    model = eval(cfg["MODEL"]["NAME"])(cfg["MODEL"]["BACKBONE"], dataset.n_classes)
+    model.load_state_dict(torch.load(str(model_path), map_location="cpu"))
     model = model.to(device)
 
-    if eval_cfg['MSF']['ENABLE']:
-        acc, macc, f1, mf1, ious, miou = evaluate_msf(model, dataloader, device, eval_cfg['MSF']['SCALES'], eval_cfg['MSF']['FLIP'])
+    if eval_cfg["MSF"]["ENABLE"]:
+        acc, macc, f1, mf1, ious, miou = evaluate_msf(
+            model,
+            dataloader,
+            device,
+            eval_cfg["MSF"]["SCALES"],
+            eval_cfg["MSF"]["FLIP"],
+        )
     else:
         acc, macc, f1, mf1, ious, miou = evaluate(model, dataloader, device)
 
     table = {
-        'Class': list(dataset.CLASSES) + ['Mean'],
-        'IoU': ious + [miou],
-        'F1': f1 + [mf1],
-        'Acc': acc + [macc]
+        "Class": list(dataset.CLASSES) + ["Mean"],
+        "IoU": ious + [miou],
+        "F1": f1 + [mf1],
+        "Acc": acc + [macc],
     }
 
-    print(tabulate(table, headers='keys'))
+    print(tabulate(table, headers="keys"))
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='configs/custom.yaml')
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        formatter_class=ArgumentDefaultsHelpFormatter,
+        description="Evaluate the model",
+    )
+    parser.add_argument("--cfg", type=str, default="configs/custom.yaml")
     args = parser.parse_args()
 
     with open(args.cfg) as f:
         cfg = yaml.load(f, Loader=yaml.SafeLoader)
 
     setup_cudnn()
-    main(cfg)
\ No newline at end of file
+    main(cfg)
diff --git a/semseg/utils/visualize.py b/semseg/utils/visualize.py
index 19ef495a..7bfa781b 100644
--- a/semseg/utils/visualize.py
+++ b/semseg/utils/visualize.py
@@ -1,20 +1,19 @@
-import torch
+import os
 import random
+
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import matplotlib.pyplot as plt
+from PIL import Image, ImageDraw, ImageFont
 from torch.utils.data import DataLoader
 from torchvision import transforms as T
 from torchvision.utils import make_grid
+
 from semseg.augmentations import Compose, Normalize, RandomResizedCrop
-from PIL import Image, ImageDraw, ImageFont
 
 
-def visualize_dataset_sample(dataset, root, split='val', batch_size=4):
-    transform = Compose([
-        RandomResizedCrop((512, 512), scale=(1.0, 1.0)),
-        Normalize()
-    ])
+def visualize_dataset_sample(dataset, root, split="val", batch_size=4):
+    transform = Compose([RandomResizedCrop((512, 512), scale=(1.0, 1.0)), Normalize()])
 
     dataset = dataset(root, split=split, transform=transform)
     dataloader = DataLoader(dataset, shuffle=True, batch_size=batch_size)
@@ -30,37 +29,168 @@ def visualize_dataset_sample(dataset, root, split='val', batch_size=4):
     labels = torch.stack(labels)
 
     inv_normalize = T.Normalize(
-        mean=(-0.485/0.229, -0.456/0.224, -0.406/0.225),
-        std=(1/0.229, 1/0.224, 1/0.225)
+        mean=(-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225),
+        std=(1 / 0.229, 1 / 0.224, 1 / 0.225),
     )
     image = inv_normalize(image)
     image *= 255
     images = torch.vstack([image, labels])
-    
+
     plt.imshow(make_grid(images, nrow=4).to(torch.uint8).numpy().transpose((1, 2, 0)))
     plt.show()
 
 
 colors = [
-    [120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-    [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-    [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-    [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-    [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-    [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-    [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-    [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-    [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-    [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-    [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-    [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-    [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-    [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-    [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-    [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-    [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-    [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-    [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]
+    [120, 120, 120],
+    [180, 120, 120],
+    [6, 230, 230],
+    [80, 50, 50],
+    [4, 200, 3],
+    [120, 120, 80],
+    [140, 140, 140],
+    [204, 5, 255],
+    [230, 230, 230],
+    [4, 250, 7],
+    [224, 5, 255],
+    [235, 255, 7],
+    [150, 5, 61],
+    [120, 120, 70],
+    [8, 255, 51],
+    [255, 6, 82],
+    [143, 255, 140],
+    [204, 255, 4],
+    [255, 51, 7],
+    [204, 70, 3],
+    [0, 102, 200],
+    [61, 230, 250],
+    [255, 6, 51],
+    [11, 102, 255],
+    [255, 7, 71],
+    [255, 9, 224],
+    [9, 7, 230],
+    [220, 220, 220],
+    [255, 9, 92],
+    [112, 9, 255],
+    [8, 255, 214],
+    [7, 255, 224],
+    [255, 184, 6],
+    [10, 255, 71],
+    [255, 41, 10],
+    [7, 255, 255],
+    [224, 255, 8],
+    [102, 8, 255],
+    [255, 61, 6],
+    [255, 194, 7],
+    [255, 122, 8],
+    [0, 255, 20],
+    [255, 8, 41],
+    [255, 5, 153],
+    [6, 51, 255],
+    [235, 12, 255],
+    [160, 150, 20],
+    [0, 163, 255],
+    [140, 140, 140],
+    [250, 10, 15],
+    [20, 255, 0],
+    [31, 255, 0],
+    [255, 31, 0],
+    [255, 224, 0],
+    [153, 255, 0],
+    [0, 0, 255],
+    [255, 71, 0],
+    [0, 235, 255],
+    [0, 173, 255],
+    [31, 0, 255],
+    [11, 200, 200],
+    [255, 82, 0],
+    [0, 255, 245],
+    [0, 61, 255],
+    [0, 255, 112],
+    [0, 255, 133],
+    [255, 0, 0],
+    [255, 163, 0],
+    [255, 102, 0],
+    [194, 255, 0],
+    [0, 143, 255],
+    [51, 255, 0],
+    [0, 82, 255],
+    [0, 255, 41],
+    [0, 255, 173],
+    [10, 0, 255],
+    [173, 255, 0],
+    [0, 255, 153],
+    [255, 92, 0],
+    [255, 0, 255],
+    [255, 0, 245],
+    [255, 0, 102],
+    [255, 173, 0],
+    [255, 0, 20],
+    [255, 184, 184],
+    [0, 31, 255],
+    [0, 255, 61],
+    [0, 71, 255],
+    [255, 0, 204],
+    [0, 255, 194],
+    [0, 255, 82],
+    [0, 10, 255],
+    [0, 112, 255],
+    [51, 0, 255],
+    [0, 194, 255],
+    [0, 122, 255],
+    [0, 255, 163],
+    [255, 153, 0],
+    [0, 255, 10],
+    [255, 112, 0],
+    [143, 255, 0],
+    [82, 0, 255],
+    [163, 255, 0],
+    [255, 235, 0],
+    [8, 184, 170],
+    [133, 0, 255],
+    [0, 255, 92],
+    [184, 0, 255],
+    [255, 0, 31],
+    [0, 184, 255],
+    [0, 214, 255],
+    [255, 0, 112],
+    [92, 255, 0],
+    [0, 224, 255],
+    [112, 224, 255],
+    [70, 184, 160],
+    [163, 0, 255],
+    [153, 0, 255],
+    [71, 255, 0],
+    [255, 0, 163],
+    [255, 204, 0],
+    [255, 0, 143],
+    [0, 255, 235],
+    [133, 255, 0],
+    [255, 0, 235],
+    [245, 0, 255],
+    [255, 0, 122],
+    [255, 245, 0],
+    [10, 190, 212],
+    [214, 255, 0],
+    [0, 204, 255],
+    [20, 0, 255],
+    [255, 255, 0],
+    [0, 153, 255],
+    [0, 41, 255],
+    [0, 255, 204],
+    [41, 0, 255],
+    [41, 255, 0],
+    [173, 0, 255],
+    [0, 245, 255],
+    [71, 0, 255],
+    [122, 0, 255],
+    [0, 255, 184],
+    [0, 92, 255],
+    [184, 255, 0],
+    [0, 133, 255],
+    [255, 214, 0],
+    [25, 194, 194],
+    [102, 255, 0],
+    [92, 0, 255],
 ]
 
 
@@ -68,15 +198,21 @@ def generate_palette(num_classes, background: bool = False):
     random.shuffle(colors)
     if background:
         palette = [[0, 0, 0]]
-        palette += colors[:num_classes-1]
+        palette += colors[: num_classes - 1]
     else:
         palette = colors[:num_classes]
     return np.array(palette)
 
 
-def draw_text(image: torch.Tensor, seg_map: torch.Tensor, labels: list, fontsize: int = 15):
+def draw_text(
+    image: torch.Tensor, seg_map: torch.Tensor, labels: list, fontsize: int = 15
+):
     image = image.to(torch.uint8)
-    font = ImageFont.truetype("assests/Helvetica.ttf", fontsize)
+    font = (
+        ImageFont.truetype("assests/Helvetica.ttf", fontsize)
+        if os.path.isfile("assests/Helvetica.ttf")
+        else ImageFont.load_default(fontsize)
+    )
     pil_image = Image.fromarray(image.numpy())
     draw = ImageDraw.Draw(pil_image)
 
@@ -88,7 +224,7 @@ def draw_text(image: torch.Tensor, seg_map: torch.Tensor, labels: list, fontsize
         mask = mask.squeeze().numpy()
         center = np.median((mask == 1).nonzero(), axis=1)[::-1]
         bbox = draw.textbbox(center, cls, font=font)
-        bbox = (bbox[0]-3, bbox[1]-3, bbox[2]+3, bbox[3]+3)
+        bbox = (bbox[0] - 3, bbox[1] - 3, bbox[2] + 3, bbox[3] + 3)
         draw.rectangle(bbox, fill=(255, 255, 255), width=1)
         draw.text(center, cls, fill=(0, 0, 0), font=font)
-    return pil_image
\ No newline at end of file
+    return pil_image
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 34eb311a..00000000
--- a/setup.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='semseg',
-    version='0.4.1',
-    description='SOTA Semantic Segmentation Models',
-    url='https://github.com/sithu31296/semantic-segmentation',
-    author='Sithu Aung',
-    author_email='sithu31296@gmail.com',
-    license='MIT',
-    packages=find_packages(include=['semseg']),
-    install_requires=[
-        'tqdm',
-        'tabulate',
-        'numpy',
-        'scipy',
-        'matplotlib',
-        'tensorboard',
-        'fvcore',
-        'einops',
-        'rich',
-    ]
-)
\ No newline at end of file
diff --git a/tools/benchmark.py b/tools/benchmark.py
deleted file mode 100644
index e45a1fa7..00000000
--- a/tools/benchmark.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import torch
-import argparse
-import time
-from fvcore.nn import flop_count_table, FlopCountAnalysis
-from semseg.models import *
-
-
-def main(
-    model_name: str,
-    backbone_name: str,
-    image_size: list,
-    num_classes: int,
-    device: str,
-):
-    device = torch.device('cuda' if torch.cuda.is_available() and device == 'cuda' else 'cpu')
-    inputs = torch.randn(1, 3, *image_size).to(device)
-    model = eval(model_name)(backbone_name, num_classes)
-    model = model.to(device)
-    model.eval()
-
-    print(flop_count_table(FlopCountAnalysis(model, inputs)))
-
-    total_time = 0.0
-    for _ in range(10):
-        tic = time.perf_counter()
-        model(inputs)
-        toc = time.perf_counter()
-        total_time += toc - tic
-    total_time /= 10
-    print(f"Inference time: {total_time*1000:.2f}ms")
-    print(f"FPS: {1/total_time}")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model-name', type=str, default='SegFormer')
-    parser.add_argument('--backbone-name', type=str, default='MiT-B0')
-    parser.add_argument('--image-size', type=list, default=[512, 512])
-    parser.add_argument('--num-classes', type=int, default=11)
-    parser.add_argument('--device', type=str, default='cuda')
-    args = parser.parse_args()
-
-    main(args.model_name, args.backbone_name, args.image_size, args.num_classes, args.device)
\ No newline at end of file
diff --git a/tools/export.py b/tools/export.py
deleted file mode 100644
index 6ecb7cbb..00000000
--- a/tools/export.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import torch
-import argparse
-import yaml
-import onnx
-from pathlib import Path
-from onnxsim import simplify
-from semseg.models import *
-from semseg.datasets import *
-
-
-def export_onnx(model, inputs, file):
-    torch.onnx.export(
-        model,
-        inputs,
-        f"{cfg['TEST']['MODEL_PATH'].split('.')[0]}.onnx",
-        input_names=['input'],
-        output_names=['output'],
-        opset_version=13
-    )
-    onnx_model = onnx.load(f"{file}.onnx")
-    onnx.checker.check_model(onnx_model)
-
-    onnx_model, check = simplify(onnx_model)
-    onnx.save(onnx_model, f"{file}.onnx")
-    assert check, "Simplified ONNX model could not be validated"
-    print(f"ONNX model saved to {file}.onnx")
-
-
-def export_coreml(model, inputs, file):
-    try:
-        import coremltools as ct
-        ts_model = torch.jit.trace(model, inputs, strict=True)
-        ct_model = ct.convert(
-            ts_model,
-            inputs=[ct.ImageType('image', shape=inputs.shape, scale=1/255.0, bias=[0, 0, 0])]
-        )
-        ct_model.save(f"{file}.mlmodel")
-        print(f"CoreML model saved to {file}.mlmodel")
-    except:
-        print("Please install coremltools to export to CoreML.\n`pip install coremltools`")
-    
-
-def main(cfg):
-    model = eval(cfg['MODEL']['NAME'])(cfg['MODEL']['BACKBONE'], len(eval(cfg['DATASET']['NAME']).PALETTE))
-    model.load_state_dict(torch.load(cfg['TEST']['MODEL_PATH'], map_location='cpu'))
-    model.eval()
-
-    inputs = torch.randn(1, 3, *cfg['TEST']['IMAGE_SIZE'])
-    file = cfg['TEST']['MODEL_PATH'].split('.')[0]
-
-    export_onnx(model, inputs, file)
-    export_coreml(model, inputs, file)
-    print(f"Finished converting.")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='configs/custom.yaml')
-    args = parser.parse_args()
-
-    with open(args.cfg) as f:
-        cfg = yaml.load(f, Loader=yaml.SafeLoader)
-
-    save_dir = Path(cfg['SAVE_DIR'])
-    save_dir.mkdir(exist_ok=True)
-    
-    main(cfg)
\ No newline at end of file
diff --git a/tools/train.py b/tools/train.py
deleted file mode 100644
index 214078cf..00000000
--- a/tools/train.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import torch 
-import argparse
-import yaml
-import time
-import multiprocessing as mp
-from tabulate import tabulate
-from tqdm import tqdm
-from torch.utils.data import DataLoader
-from pathlib import Path
-from torch.utils.tensorboard import SummaryWriter
-from torch.cuda.amp import GradScaler, autocast
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data import DistributedSampler, RandomSampler
-from torch import distributed as dist
-from semseg.models import *
-from semseg.datasets import * 
-from semseg.augmentations import get_train_augmentation, get_val_augmentation
-from semseg.losses import get_loss
-from semseg.schedulers import get_scheduler
-from semseg.optimizers import get_optimizer
-from semseg.utils.utils import fix_seeds, setup_cudnn, cleanup_ddp, setup_ddp
-from val import evaluate
-
-
-def main(cfg, gpu, save_dir):
-    start = time.time()
-    best_mIoU = 0.0
-    num_workers = mp.cpu_count()
-    device = torch.device(cfg['DEVICE'])
-    train_cfg, eval_cfg = cfg['TRAIN'], cfg['EVAL']
-    dataset_cfg, model_cfg = cfg['DATASET'], cfg['MODEL']
-    loss_cfg, optim_cfg, sched_cfg = cfg['LOSS'], cfg['OPTIMIZER'], cfg['SCHEDULER']
-    epochs, lr = train_cfg['EPOCHS'], optim_cfg['LR']
-    
-    traintransform = get_train_augmentation(train_cfg['IMAGE_SIZE'], seg_fill=dataset_cfg['IGNORE_LABEL'])
-    valtransform = get_val_augmentation(eval_cfg['IMAGE_SIZE'])
-
-    trainset = eval(dataset_cfg['NAME'])(dataset_cfg['ROOT'], 'train', traintransform)
-    valset = eval(dataset_cfg['NAME'])(dataset_cfg['ROOT'], 'val', valtransform)
-    
-    model = eval(model_cfg['NAME'])(model_cfg['BACKBONE'], trainset.n_classes)
-    model.init_pretrained(model_cfg['PRETRAINED'])
-    model = model.to(device)
-
-    if train_cfg['DDP']: 
-        sampler = DistributedSampler(trainset, dist.get_world_size(), dist.get_rank(), shuffle=True)
-        model = DDP(model, device_ids=[gpu])
-    else:
-        sampler = RandomSampler(trainset)
-    
-    trainloader = DataLoader(trainset, batch_size=train_cfg['BATCH_SIZE'], num_workers=num_workers, drop_last=True, pin_memory=True, sampler=sampler)
-    valloader = DataLoader(valset, batch_size=1, num_workers=1, pin_memory=True)
-
-    iters_per_epoch = len(trainset) // train_cfg['BATCH_SIZE']
-    # class_weights = trainset.class_weights.to(device)
-    loss_fn = get_loss(loss_cfg['NAME'], trainset.ignore_label, None)
-    optimizer = get_optimizer(model, optim_cfg['NAME'], lr, optim_cfg['WEIGHT_DECAY'])
-    scheduler = get_scheduler(sched_cfg['NAME'], optimizer, epochs * iters_per_epoch, sched_cfg['POWER'], iters_per_epoch * sched_cfg['WARMUP'], sched_cfg['WARMUP_RATIO'])
-    scaler = GradScaler(enabled=train_cfg['AMP'])
-    writer = SummaryWriter(str(save_dir / 'logs'))
-
-    for epoch in range(epochs):
-        model.train()
-        if train_cfg['DDP']: sampler.set_epoch(epoch)
-
-        train_loss = 0.0
-        pbar = tqdm(enumerate(trainloader), total=iters_per_epoch, desc=f"Epoch: [{epoch+1}/{epochs}] Iter: [{0}/{iters_per_epoch}] LR: {lr:.8f} Loss: {train_loss:.8f}")
-
-        for iter, (img, lbl) in pbar:
-            optimizer.zero_grad(set_to_none=True)
-
-            img = img.to(device)
-            lbl = lbl.to(device)
-            
-            with autocast(enabled=train_cfg['AMP']):
-                logits = model(img)
-                loss = loss_fn(logits, lbl)
-
-            scaler.scale(loss).backward()
-            scaler.step(optimizer)
-            scaler.update()
-            scheduler.step()
-            torch.cuda.synchronize()
-
-            lr = scheduler.get_lr()
-            lr = sum(lr) / len(lr)
-            train_loss += loss.item()
-
-            pbar.set_description(f"Epoch: [{epoch+1}/{epochs}] Iter: [{iter+1}/{iters_per_epoch}] LR: {lr:.8f} Loss: {train_loss / (iter+1):.8f}")
-        
-        train_loss /= iter+1
-        writer.add_scalar('train/loss', train_loss, epoch)
-        torch.cuda.empty_cache()
-
-        if (epoch+1) % train_cfg['EVAL_INTERVAL'] == 0 or (epoch+1) == epochs:
-            miou = evaluate(model, valloader, device)[-1]
-            writer.add_scalar('val/mIoU', miou, epoch)
-
-            if miou > best_mIoU:
-                best_mIoU = miou
-                torch.save(model.module.state_dict() if train_cfg['DDP'] else model.state_dict(), save_dir / f"{model_cfg['NAME']}_{model_cfg['BACKBONE']}_{dataset_cfg['NAME']}.pth")
-            print(f"Current mIoU: {miou} Best mIoU: {best_mIoU}")
-
-    writer.close()
-    pbar.close()
-    end = time.gmtime(time.time() - start)
-
-    table = [
-        ['Best mIoU', f"{best_mIoU:.2f}"],
-        ['Total Training Time', time.strftime("%H:%M:%S", end)]
-    ]
-    print(tabulate(table, numalign='right'))
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='configs/custom.yaml', help='Configuration file to use')
-    args = parser.parse_args()
-
-    with open(args.cfg) as f:
-        cfg = yaml.load(f, Loader=yaml.SafeLoader)
-
-    fix_seeds(3407)
-    setup_cudnn()
-    gpu = setup_ddp()
-    save_dir = Path(cfg['SAVE_DIR'])
-    save_dir.mkdir(exist_ok=True)
-    main(cfg, gpu, save_dir)
-    cleanup_ddp()
\ No newline at end of file
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index 00000000..abf1a363
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