Update README.md (#272)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/nn/autobackend.py

@@ -197,7 +197,7 @@ class AutoBackend(nn.Module):
             input_details = interpreter.get_input_details()  # inputs
             output_details = interpreter.get_output_details()  # outputs
         elif tfjs:  # TF.js
-            raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
+            raise NotImplementedError('ERROR: YOLOv8 TF.js inference is not supported')
         elif paddle:  # PaddlePaddle
             LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
             check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')

ultralytics/nn/modules.py

@@ -366,7 +366,7 @@ class Concat(nn.Module):
 
 
 class AutoShape(nn.Module):
-    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    # YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
     agnostic = False  # NMS class-agnostic
@@ -465,7 +465,7 @@ class AutoShape(nn.Module):
 
 
 class Detections:
-    # YOLOv5 detections class for inference results
+    # YOLOv8 detections class for inference results
     def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
         super().__init__()
         d = pred[0].device  # device
@@ -572,7 +572,7 @@ class Detections:
         return self._run(pprint=True)  # print results
 
     def __repr__(self):
-        return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
+        return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
 
 
 class Proto(nn.Module):
@@ -603,7 +603,7 @@ class Ensemble(nn.ModuleList):
 
 # heads
 class Detect(nn.Module):
-    # YOLOv5 Detect head for detection models
+    # YOLOv8 Detect head for detection models
     dynamic = False  # force grid reconstruction
     export = False  # export mode
     shape = None
@@ -650,7 +650,7 @@ class Detect(nn.Module):
 
 
 class Segment(Detect):
-    # YOLOv5 Segment head for segmentation models
+    # YOLOv8 Segment head for segmentation models
     def __init__(self, nc=80, nm=32, npr=256, ch=()):
         super().__init__(nc, ch)
         self.nm = nm  # number of masks
@@ -673,7 +673,7 @@ class Segment(Detect):
 
 
 class Classify(nn.Module):
-    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    # YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
         super().__init__()
         c_ = 1280  # efficientnet_b0 size

ultralytics/nn/tasks.py

@@ -142,7 +142,7 @@ class BaseModel(nn.Module):
 
 
 class DetectionModel(BaseModel):
-    # YOLOv5 detection model
+    # YOLOv8 detection model
     def __init__(self, cfg='yolov8n.yaml', ch=3, nc=None, verbose=True):  # model, input channels, number of classes
         super().__init__()
         self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True)  # cfg dict
@@ -222,13 +222,13 @@ class DetectionModel(BaseModel):
 
 
 class SegmentationModel(DetectionModel):
-    # YOLOv5 segmentation model
+    # YOLOv8 segmentation model
     def __init__(self, cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True):
         super().__init__(cfg, ch, nc, verbose)
 
 
 class ClassificationModel(BaseModel):
-    # YOLOv5 classification model
+    # YOLOv8 classification model
     def __init__(self,
                  cfg=None,
                  model=None,