Merge branch 'main' of github.com:iMoonLab/Hyper-YOLO

ultralytics/cfg/models/hyper-yolo/hyper-yolo-seg.yaml

@@ -1,9 +1,9 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# Hyper-YOLO object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+# Hyper-YOLO object detection model with N3-N5 outputs. 
 
 # Parameters
 nc: 80  # number of classes
-scales: # model compound scaling constants, i.e. 'model=hyper-yolon.yaml' will call hyper-yolo.yaml with scale 'n'
+scales: # model compound scaling constants, i.e. 'model=hyper-yolon-seg.yaml' will call hyper-yolo-seg.yaml with scale 'n'
   # [depth, width, max_channels, threshold]
   n: [0.33, 0.25, 1024, 6]
   s: [0.33, 0.50, 1024, 9]

ultralytics/cfg/models/hyper-yolo/hyper-yolo.yaml

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# Hyper-YOLO object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+# Hyper-YOLO object detection model with N3-N5 outputs. 
 
 # Parameters
 nc: 80  # number of classes
@@ -44,14 +44,14 @@ head:
 
   - [17, 1, nn.Upsample, [None, 2, 'nearest']] # 23
   - [[-1, 4], 1, Concat, [1]]  # cat 24
-  - [-1, 3, MANet, [256, False, 2, 3]]  # 25 P3
+  - [-1, 3, MANet, [256, False, 2, 3]]  # 25 N3
 
   - [-1, 1, Conv, [256, 3, 2]] # 26
   - [[-1, 22], 1, Concat, [1]]  # 27 cat head
-  - [-1, 3, MANet, [512, False, 2, 3]]  # 28
+  - [-1, 3, MANet, [512, False, 2, 3]]  # 28 N4
 
   - [-1, 1, Conv, [512, 3, 2]] # 29
   - [[-1, 20], 1, Concat, [1]]  # 30 cat head P5
-  - [-1, 3, C2f, [1024, False]]  # 31
+  - [-1, 3, C2f, [1024, False]]  # 31 N5
 
   - [[25, 28, 31], 1, Detect, [nc]]  # Detect(N3, N4, N5)

ultralytics/cfg/models/hyper-yolo/hyper-yolot.yaml

@@ -0,0 +1,52 @@
+# HyperYOLO object detection model with N3-N5 outputs.
+
+# Parameters
+nc: 80  # number of classes
+scales: # model compound scaling constants,
+  # [depth, width, max_channels, threshold]
+  t: [0.33, 0.25, 1024, 6]
+
+# backbone
+backbone:
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-B1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1
+  - [-1, 3, C2f, [128, True]] # 2-B2/4
+  - [-1, 1, Conv, [256, 3, 2]]  # 3
+  - [-1, 6, MANet, [256, True, 1, 3]] # 4-B3/8
+  - [-1, 1, Conv, [512, 3, 2]]  # 5
+  - [-1, 6, MANet, [512, True, 1, 3]] # 6-B4/16
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]] # 8
+  - [-1, 1, SPPF, [1024, 5]]  # 9-B5/32
+
+# head
+head:
+ - [0, 1, nn.AvgPool2d, [8, 8, 0]] # 10
+ - [2, 1, nn.AvgPool2d, [4, 4, 0]] # 11
+ - [4, 1, nn.AvgPool2d, [2, 2, 0]] # 12
+ - [9, 1, nn.Upsample, [None, 2, 'nearest']] # 13
+ - [[10, 11, 12, 6, 13], 1, Concat, [1]]  # cat 14
+ - [-1, 1, Conv, [512, 1, 1]] # 15
+ - [-1, 1, HyperComputeModule, [512]] # 16
+ - [-1, 3, MANet, [512, True]] # 17
+
+ - [-1, 1, nn.AvgPool2d, [2, 2, 0]] # 18
+ - [[-1, 9], 1, Concat, [1]]  # cat 19
+ - [-1, 1, Conv, [1024, 1, 1]] # 20
+
+ - [[17, 6], 1, Concat, [1]]  # cat 21
+ - [-1, 3, C2f, [512, False]]  # 22
+
+ - [17, 1, nn.Upsample, [None, 2, 'nearest']] # 23
+ - [[-1, 4], 1, Concat, [1]]  # cat 24
+ - [-1, 3, C2f, [256, False]]  # 25
+
+ - [-1, 1, Conv, [256, 3, 2]] # 26
+ - [[-1, 22], 1, Concat, [1]]  # 27 cat 
+ - [-1, 3, C2f, [512, False]]  # 28
+
+ - [-1, 1, Conv, [512, 3, 2]] # 29
+ - [[-1, 20], 1, Concat, [1]]  # 30 cat 
+ - [-1, 1, Conv, [1024, 1, 1]]  # 31
+
+ - [[25, 28, 31], 1, Detect, [nc]]  # Detect(N3, N4, N5)

ultralytics/nn/tasks.py

@@ -746,6 +746,7 @@ def yaml_model_load(path):
 
     unified_path = re.sub(r'(\d+)([nslmx])(.+)?$', r'\1\3', str(path))  # i.e. yolov8x.yaml -> yolov8.yaml
     unified_path = re.sub(r'(hyper-yolo)([nslmx])(.+)?$', r'\1\3', str(unified_path))
+    unified_path = re.sub(r'(hyper-yolot)(.+)?$', r'\1\2', str(unified_path))
 
     yaml_file = check_yaml(unified_path, hard=False) or check_yaml(path)
     d = yaml_load(yaml_file)  # model dict
@@ -769,8 +770,8 @@ def guess_model_scale(model_path):
     """
     with contextlib.suppress(AttributeError):
         import re
-        if re.search(r'(hyper-yolo)([nslmx])', Path(model_path).stem) is not None:
-            return re.search(r'(hyper-yolo)([nslmx])', Path(model_path).stem).group(2)
+        if re.search(r'(hyper-yolo)([tnslmx])', Path(model_path).stem) is not None:
+            return re.search(r'(hyper-yolo)([tnslmx])', Path(model_path).stem).group(2)
         else:
             return re.search(r'yolov\d+([nslmx])', Path(model_path).stem).group(1)  # n, s, m, l, or x
     return ''