last changes

data/lpr.yaml

@@ -0,0 +1,21 @@
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
+# Train command: python train.py --data voc.yaml
+# Default dataset location is next to /yolov5:
+#   /parent_folder
+#     /VOC
+#     /yolov5
+
+
+# download command/URL (optional)
+download: bash data/scripts/get_voc.sh
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: D:/Datasets/Board/small/train  # 16551 images
+val: D:/Datasets/Board/small/val  # 16551 images
+#val: /ssd_1t/derron/yolov5-face/data/widerface/train/  # 4952 images
+
+# number of classes
+nc: 1
+
+# class names
+names: ['front']

models/polygon_yolov5l.yaml → models/polygon_yolov5n.yaml

@@ -1,30 +1,30 @@
-# parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 
-# anchors
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.25  # layer channel multiple
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
@@ -44,5 +44,5 @@ head:
    [[-1, 10], 1, Concat, [1]],  # cat head P5
    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
-   [[17, 20, 23], 1, Polygon_Detect, [nc, anchors]],  # Polygon_Detect(P3, P4, P5)
+   [[17, 20, 23], 1, Polygon_Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

models/yolo_polygon.py

@@ -13,6 +13,10 @@
 sys.path.append(Path(__file__).parent.parent.absolute().__str__())  # to run '$ python *.py' files in subdirectories
 logger = logging.getLogger(__name__)
 
+from models.common import *
+from utils.general_polygon import polygon_non_max_suppression, polygon_scale_coords
+from utils.plots_polygon import polygon_plot_one_box
+
 from models.yolo import *
 from models.experimental import *
 from utils.autoanchor_polygon import check_anchor_order
@@ -42,8 +46,8 @@ def polygon_parse_model(d, ch):  # model_dict, input_channels(3)
                 pass
 
         n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
-                 C3, C3TR]:
+        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
+                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
             c1, c2 = ch[f], args[0]
             if c2 != no:  # if not output
                 c2 = make_divisible(c2 * gw, 8)
@@ -222,10 +226,6 @@ def autoshape(self):  # add Polygon_AutoShape module
         copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
         return m
 
-from models.common import *
-from utils.general_polygon import polygon_non_max_suppression, polygon_scale_coords
-from utils.plots_polygon import polygon_plot_one_box
-
 # Ancillary functions with polygon anchor boxes-------------------------------------------------------------------------------------------
 
 class Polygon_NMS(nn.Module):
@@ -392,32 +392,32 @@ def __len__(self):
         return self.n
 
 
-class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super(GhostConv, self).__init__()
-        c_ = c2 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, k, s, None, g, act)
-        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
-    def forward(self, x):
-        y = self.cv1(x)
-        return torch.cat([y, self.cv2(y)], 1)
-
-
-class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super(GhostBottleneck, self).__init__()
-        c_ = c2 // 2
-        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
-                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
-                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
-
-    def forward(self, x):
-        return self.conv(x) + self.shortcut(x)
+# class GhostConv(nn.Module):
+#     # Ghost Convolution https://github.com/huawei-noah/ghostnet
+#     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
+#         super(GhostConv, self).__init__()
+#         c_ = c2 // 2  # hidden channels
+#         self.cv1 = Conv(c1, c_, k, s, None, g, act)
+#         self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+#     def forward(self, x):
+#         y = self.cv1(x)
+#         return torch.cat([y, self.cv2(y)], 1)
+
+
+# class GhostBottleneck(nn.Module):
+#     # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+#     def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
+#         super(GhostBottleneck, self).__init__()
+#         c_ = c2 // 2
+#         self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
+#                                   DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+#                                   GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+#         self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
+#                                       Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+
+#     def forward(self, x):
+#         return self.conv(x) + self.shortcut(x)