`process_mask_native()` cleanup
diff --git a/segment/predict.py b/segment/predict.py
index 4d8458fd879..4ba9e46ddab 100644
--- a/segment/predict.py
+++ b/segment/predict.py
@@ -44,7 +44,7 @@
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
-                           strip_optimizer, xyxy2xywh)
+                           strip_optimizer)
 from utils.plots import Annotator, colors, save_one_box
 from utils.segment.general import masks2segments, process_mask, process_mask_native
 from utils.torch_utils import select_device, smart_inference_mode
@@ -161,10 +161,9 @@ def run(
 
                 # Segments
                 if save_txt:
-                    segments = reversed(masks2segments(masks))
                     segments = [
                         scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
-                        for x in segments]
+                        for x in reversed(masks2segments(masks))]
 
                 # Print results
                 for c in det[:, 5].unique():
@@ -172,15 +171,17 @@ def run(
                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
 
                 # Mask plotting
-                plot_img = torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255. \
-                        if retina_masks else im[i]
-                annotator.masks(masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=plot_img)
+                annotator.masks(
+                    masks,
+                    colors=[colors(x, True) for x in det[:, 5]],
+                    im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() /
+                    255 if retina_masks else im[i])
 
                 # Write results
                 for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                     if save_txt:  # Write to file
-                        segj = segments[j].reshape(-1)  # (n,2) to (n*2)
-                        line = (cls, *segj, conf) if save_conf else (cls, *segj)  # label format
+                        seg = segments[j].reshape(-1)  # (n,2) to (n*2)
+                        line = (cls, *seg, conf) if save_conf else (cls, *seg)  # label format
                         with open(f'{txt_path}.txt', 'a') as f:
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
diff --git a/segment/val.py b/segment/val.py
index 48bf28d4bf4..368a058f9ce 100644
--- a/segment/val.py
+++ b/segment/val.py
@@ -48,7 +48,7 @@
 from utils.metrics import ConfusionMatrix, box_iou
 from utils.plots import output_to_target, plot_val_study
 from utils.segment.dataloaders import create_dataloader
-from utils.segment.general import mask_iou, process_mask, process_mask_upsample, scale_image
+from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
 from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
 from utils.segment.plots import plot_images_and_masks
 from utils.torch_utils import de_parallel, select_device, smart_inference_mode
@@ -160,7 +160,7 @@ def run(
 ):
     if save_json:
         check_requirements(['pycocotools'])
-        process = process_mask_upsample  # more accurate
+        process = process_mask_native  # more accurate
     else:
         process = process_mask  # faster
 
@@ -312,7 +312,7 @@ def run(
 
             pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
             if plots and batch_i < 3:
-                plot_masks.append(pred_masks[:15].cpu())  # filter top 15 to plot
+                plot_masks.append(pred_masks[:15])  # filter top 15 to plot
 
             # Save/log
             if save_txt:
@@ -367,8 +367,8 @@ def run(
     # Save JSON
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
-        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
+        anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json'))  # annotations
+        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions
         LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f:
             json.dump(jdict, f)
diff --git a/utils/segment/general.py b/utils/segment/general.py
index 6ebfd27bd9d..9da89453866 100644
--- a/utils/segment/general.py
+++ b/utils/segment/general.py
@@ -25,10 +25,10 @@ def crop_mask(masks, boxes):
 def process_mask_upsample(protos, masks_in, bboxes, shape):
     """
     Crop after upsample.
-    proto_out: [mask_dim, mask_h, mask_w]
-    out_masks: [n, mask_dim], n is number of masks after nms
+    protos: [mask_dim, mask_h, mask_w]
+    masks_in: [n, mask_dim], n is number of masks after nms
     bboxes: [n, 4], n is number of masks after nms
-    shape:input_image_size, (h, w)
+    shape: input_image_size, (h, w)
 
     return: h, w, n
     """
@@ -67,25 +67,25 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
     return masks.gt_(0.5)
 
 
-def process_mask_native(protos, masks_in, bboxes, dst_shape):
+def process_mask_native(protos, masks_in, bboxes, shape):
     """
     Crop after upsample.
-    proto_out: [mask_dim, mask_h, mask_w]
-    out_masks: [n, mask_dim], n is number of masks after nms
+    protos: [mask_dim, mask_h, mask_w]
+    masks_in: [n, mask_dim], n is number of masks after nms
     bboxes: [n, 4], n is number of masks after nms
-    shape:input_image_size, (h, w)
+    shape: input_image_size, (h, w)
 
     return: h, w, n
     """
     c, mh, mw = protos.shape  # CHW
     masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
-    gain = min(mh / dst_shape[0], mw / dst_shape[1])  # gain  = old / new
-    pad = (mw - dst_shape[1] * gain) / 2, (mh - dst_shape[0] * gain) / 2  # wh padding
+    gain = min(mh / shape[0], mw / shape[1])  # gain  = old / new
+    pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2  # wh padding
     top, left = int(pad[1]), int(pad[0])  # y, x
     bottom, right = int(mh - pad[1]), int(mw - pad[0])
     masks = masks[:, top:bottom, left:right]
 
-    masks = F.interpolate(masks[None], dst_shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
     masks = crop_mask(masks, bboxes)  # CHW
     return masks.gt_(0.5)
 
diff --git a/val.py b/val.py
index 7c610e83a85..e84249ed383 100644
--- a/val.py
+++ b/val.py
@@ -302,8 +302,8 @@ def run(
     # Save JSON
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
-        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
+        anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json'))  # annotations
+        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions
         LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f:
             json.dump(jdict, f)

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## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Refactoring and simplification of segmentation mask processing in the YOLOv5 repository.

### 📊 Key Changes
- Removal of `xyxy2xywh` import as it's no longer needed.
- Reorganization of segment scaling code in `predict.py` for clarity.
- Introduction of `process_mask_native` function in `val.py` and `general.py` as a replacement for `process_mask_upsample` for a more accurate mask processing.
- Use of native tensor operations for mask plotting in `predict.py` replacing explicit type conversion.
- Changed the paths in `val.py` and `val.py` for loading annotations JSON for cleaner code.

### 🎯 Purpose & Impact
- To streamline the codebase, making it simpler and more maintainable.
- To boost the accuracy of mask processing by leveraging native functions.
- To reduce complexity and potential for type-related bugs in mask plotting.
- These changes improve the reliability and efficiency of the code, which could enhance the performance for end users working with semantic segmentation tasks in YOLOv5. 🚀