diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index ccc4c10391..aaf9fdd21e 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -5,6 +5,8 @@
 name: Ultralytics Actions
 
 on:
+  issues:
+    types: [opened, edited]
   pull_request_target:
     branches: [main]
     types: [opened, closed, synchronize]
@@ -16,7 +18,8 @@ jobs:
       - name: Run Ultralytics Formatting
         uses: ultralytics/actions@main
         with:
-          token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, do not modify
+          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
+          labels: true # autolabel issues and PRs
           python: true # format Python code and docstrings
           markdown: true # format Markdown
           prettier: true # format YAML
diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
index 73542b3a3d..216250fba9 100644
--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@@ -52,6 +52,7 @@ jobs:
             --exclude-path docs/hi \
             --exclude-path docs/ar \
             --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --header "User-Agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
             './**/*.md' \
             './**/*.html'
 
@@ -82,6 +83,7 @@ jobs:
             --exclude-path docs/hi \
             --exclude-path docs/ar \
             --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --header "User-Agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
             './**/*.md' \
             './**/*.html' \
             './**/*.yml' \
diff --git a/docs/build_docs.py b/docs/build_docs.py
index 67ec5bd1f7..181455c398 100644
--- a/docs/build_docs.py
+++ b/docs/build_docs.py
@@ -192,7 +192,11 @@ def convert_plaintext_links_to_html(content):
     for paragraph in main_content.find_all(["p", "li"]):  # Focus on paragraphs and list items
         for text_node in paragraph.find_all(string=True, recursive=False):
             if text_node.parent.name not in {"a", "code"}:  # Ignore links and code blocks
-                new_text = re.sub(r"(https?://\S+?)(?=[,.!?;:]?\s|[,.!?;:]?$)", r'<a href="\1">\1</a>', str(text_node))
+                new_text = re.sub(
+                    r'(https?://[^\s()<>]+(?:\.[^\s()<>]+)+)(?<![.,:;\'"])',
+                    r'<a href="\1">\1</a>',
+                    str(text_node),
+                )
                 if "<a" in new_text:
                     new_soup = BeautifulSoup(new_text, "html.parser")
                     text_node.replace_with(new_soup)
diff --git a/docs/en/datasets/obb/index.md b/docs/en/datasets/obb/index.md
index 11cb1ee219..b0b548b0d5 100644
--- a/docs/en/datasets/obb/index.md
+++ b/docs/en/datasets/obb/index.md
@@ -15,7 +15,7 @@ Training a precise object detection model with oriented bounding boxes (OBB) req
 The YOLO OBB format designates bounding boxes by their four corner points with coordinates normalized between 0 and 1. It follows this format:
 
 ```bash
-class_index, x1, y1, x2, y2, x3, y3, x4, y4
+class_index x1 y1 x2 y2 x3 y3 x4 y4
 ```
 
 Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation.
diff --git a/docs/en/reference/models/fastsam/utils.md b/docs/en/reference/models/fastsam/utils.md
index 43c5617c21..14695908d9 100644
--- a/docs/en/reference/models/fastsam/utils.md
+++ b/docs/en/reference/models/fastsam/utils.md
@@ -13,8 +13,4 @@ keywords: FastSAM, bounding boxes, IoU, Ultralytics, image processing, computer
 
 ## ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border
 
-<br><br><hr><br>
-
-## ::: ultralytics.models.fastsam.utils.bbox_iou
-
 <br><br>
diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml
index ca63915eba..91d2960520 100644
--- a/docs/mkdocs_github_authors.yaml
+++ b/docs/mkdocs_github_authors.yaml
@@ -36,6 +36,7 @@ plashchynski@gmail.com: plashchynski
 priytosh.revolution@live.com: priytosh-tripathi
 rulosanti@gmail.com: null
 shuizhuyuanluo@126.com: null
+sometimesocrazy@gmail.com: null
 stormsson@users.noreply.github.com: stormsson
 waxmann.sergiu@me.com: sergiuwaxmann
 web@ultralytics.com: UltralyticsAssistant
diff --git a/tests/test_python.py b/tests/test_python.py
index 97441a78f6..062716dde0 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -95,7 +95,7 @@ def test_predict_img(model_name):
         Image.open(SOURCE),  # PIL
         np.zeros((320, 640, 3), dtype=np.uint8),  # numpy
     ]
-    assert len(model(batch, imgsz=32, augment=True)) == len(batch)  # multiple sources in a batch
+    assert len(model(batch, imgsz=32)) == len(batch)  # multiple sources in a batch
 
 
 @pytest.mark.parametrize("model", MODELS)
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 3a4ab20152..362d4eead9 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.62"
+__version__ = "8.2.63"
 
 import os
 
diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py
index f7ffb2faa3..023c1f9ab8 100644
--- a/ultralytics/models/fastsam/predict.py
+++ b/ultralytics/models/fastsam/predict.py
@@ -1,84 +1,31 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-
 import torch
 
-from ultralytics.engine.results import Results
-from ultralytics.models.fastsam.utils import bbox_iou
-from ultralytics.models.yolo.detect.predict import DetectionPredictor
-from ultralytics.utils import DEFAULT_CFG, ops
+from ultralytics.models.yolo.segment import SegmentationPredictor
+from ultralytics.utils.metrics import box_iou
+
+from .utils import adjust_bboxes_to_image_border
 
 
-class FastSAMPredictor(DetectionPredictor):
+class FastSAMPredictor(SegmentationPredictor):
     """
     FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics
     YOLO framework.
 
-    This class extends the DetectionPredictor, customizing the prediction pipeline specifically for fast SAM.
-    It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing
-    for single-class segmentation.
-
-    Attributes:
-        cfg (dict): Configuration parameters for prediction.
-        overrides (dict, optional): Optional parameter overrides for custom behavior.
-        _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
+    This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It
+    adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single-
+    class segmentation.
     """
 
-    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
-        """
-        Initializes the FastSAMPredictor class, inheriting from DetectionPredictor and setting the task to 'segment'.
-
-        Args:
-            cfg (dict): Configuration parameters for prediction.
-            overrides (dict, optional): Optional parameter overrides for custom behavior.
-            _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
-        """
-        super().__init__(cfg, overrides, _callbacks)
-        self.args.task = "segment"
-
     def postprocess(self, preds, img, orig_imgs):
-        """
-        Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image
-        size, and returns the final results.
-
-        Args:
-            preds (list): The raw output predictions from the model.
-            img (torch.Tensor): The processed image tensor.
-            orig_imgs (list | torch.Tensor): The original image or list of images.
-
-        Returns:
-            (list): A list of Results objects, each containing processed boxes, masks, and other metadata.
-        """
-        p = ops.non_max_suppression(
-            preds[0],
-            self.args.conf,
-            self.args.iou,
-            agnostic=self.args.agnostic_nms,
-            max_det=self.args.max_det,
-            nc=1,  # set to 1 class since SAM has no class predictions
-            classes=self.args.classes,
-        )
-        full_box = torch.zeros(p[0].shape[1], device=p[0].device)
-        full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0
-        full_box = full_box.view(1, -1)
-        critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:])
-        if critical_iou_index.numel() != 0:
-            full_box[0][4] = p[0][critical_iou_index][:, 4]
-            full_box[0][6:] = p[0][critical_iou_index][:, 6:]
-            p[0][critical_iou_index] = full_box
-
-        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
-            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
-
-        results = []
-        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
-        for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
-            if not len(pred):  # save empty boxes
-                masks = None
-            elif self.args.retina_masks:
-                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-                masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
-            else:
-                masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
-                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+        """Applies box postprocess for FastSAM predictions."""
+        results = super().postprocess(preds, img, orig_imgs)
+        for result in results:
+            full_box = torch.tensor(
+                [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32
+            )
+            boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape)
+            idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten()
+            if idx.numel() != 0:
+                result.boxes.xyxy[idx] = full_box
         return results
diff --git a/ultralytics/models/fastsam/utils.py b/ultralytics/models/fastsam/utils.py
index 480e903942..5427083e35 100644
--- a/ultralytics/models/fastsam/utils.py
+++ b/ultralytics/models/fastsam/utils.py
@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import torch
-
 
 def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
     """
@@ -25,43 +23,3 @@ def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
     boxes[boxes[:, 2] > w - threshold, 2] = w  # x2
     boxes[boxes[:, 3] > h - threshold, 3] = h  # y2
     return boxes
-
-
-def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
-    """
-    Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
-
-    Args:
-        box1 (torch.Tensor): (4, )
-        boxes (torch.Tensor): (n, 4)
-        iou_thres (float): IoU threshold
-        image_shape (tuple): (height, width)
-        raw_output (bool): If True, return the raw IoU values instead of the indices
-
-    Returns:
-        high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
-    """
-    boxes = adjust_bboxes_to_image_border(boxes, image_shape)
-    # Obtain coordinates for intersections
-    x1 = torch.max(box1[0], boxes[:, 0])
-    y1 = torch.max(box1[1], boxes[:, 1])
-    x2 = torch.min(box1[2], boxes[:, 2])
-    y2 = torch.min(box1[3], boxes[:, 3])
-
-    # Compute the area of intersection
-    intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
-
-    # Compute the area of both individual boxes
-    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
-    box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-    # Compute the area of union
-    union = box1_area + box2_area - intersection
-
-    # Compute the IoU
-    iou = intersection / union  # Should be shape (n, )
-    if raw_output:
-        return 0 if iou.numel() == 0 else iou
-
-    # return indices of boxes with IoU > thres
-    return torch.nonzero(iou > iou_thres).flatten()
diff --git a/ultralytics/models/nas/model.py b/ultralytics/models/nas/model.py
index 9b75c60ac0..fd444f1389 100644
--- a/ultralytics/models/nas/model.py
+++ b/ultralytics/models/nas/model.py
@@ -16,6 +16,7 @@ from pathlib import Path
 import torch
 
 from ultralytics.engine.model import Model
+from ultralytics.utils.downloads import attempt_download_asset
 from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
 from .predict import NASPredictor
@@ -56,7 +57,7 @@ class NAS(Model):
 
         suffix = Path(weights).suffix
         if suffix == ".pt":
-            self.model = torch.load(weights)
+            self.model = torch.load(attempt_download_asset(weights))
         elif suffix == "":
             self.model = super_gradients.training.models.get(weights, pretrained_weights="coco")
         # Standardize model
diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py
index 853ef04816..1ca42fe8ed 100644
--- a/ultralytics/models/yolo/classify/predict.py
+++ b/ultralytics/models/yolo/classify/predict.py
@@ -54,8 +54,6 @@ class ClassificationPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
-            img_path = self.batch[0][i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
         return results
diff --git a/ultralytics/models/yolo/detect/predict.py b/ultralytics/models/yolo/detect/predict.py
index 3a0c6287ac..9842928db2 100644
--- a/ultralytics/models/yolo/detect/predict.py
+++ b/ultralytics/models/yolo/detect/predict.py
@@ -35,9 +35,7 @@ class DetectionPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            img_path = self.batch[0][i]
             results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
diff --git a/ultralytics/models/yolo/pose/predict.py b/ultralytics/models/yolo/pose/predict.py
index 7c55709f23..911c424fbb 100644
--- a/ultralytics/models/yolo/pose/predict.py
+++ b/ultralytics/models/yolo/pose/predict.py
@@ -46,12 +46,10 @@ class PosePredictor(DetectionPredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
             pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
             pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
-            img_path = self.batch[0][i]
             results.append(
                 Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
             )
diff --git a/ultralytics/models/yolo/segment/predict.py b/ultralytics/models/yolo/segment/predict.py
index 9d7015ff9f..d007eeea44 100644
--- a/ultralytics/models/yolo/segment/predict.py
+++ b/ultralytics/models/yolo/segment/predict.py
@@ -42,9 +42,7 @@ class SegmentationPredictor(DetectionPredictor):
 
         results = []
         proto = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]  # tuple if PyTorch model or array if exported
-        for i, pred in enumerate(p):
-            orig_img = orig_imgs[i]
-            img_path = self.batch[0][i]
+        for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
             if not len(pred):  # save empty boxes
                 masks = None
             elif self.args.retina_masks:
diff --git a/ultralytics/utils/downloads.py b/ultralytics/utils/downloads.py
index f2cd5e58e2..bc9ac2cb63 100644
--- a/ultralytics/utils/downloads.py
+++ b/ultralytics/utils/downloads.py
@@ -199,7 +199,7 @@ def check_disk_space(url="https://ultralytics.com/assets/coco8.zip", path=Path.c
     Check if there is sufficient disk space to download and store a file.
 
     Args:
-        url (str, optional): The URL to the file. Defaults to 'https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip'.
+        url (str, optional): The URL to the file. Defaults to 'https://ultralytics.com/assets/coco8.zip'.
         path (str | Path, optional): The path or drive to check the available free space on.
         sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.
         hard (bool, optional): Whether to throw an error or not on insufficient disk space. Defaults to True.