Added Dropout

ultralytics#12 lead to overfitting. We attempt to address this in the added segmentation path

models/common.py

@@ -856,16 +856,23 @@ def __init__(self, in_channels):
         self.cv2 = Conv(32, 64, k=3)
         self.cv3 = Conv(64, 1, act=False)
         self.relu = nn.ReLU()
+
+        self.dropout_weak= nn.Dropout(0.25)
+        self.dropout_normal = nn.Dropout(0.5)
         # self.sigmoid = nn.Sigmoid()
 
     def forward(self, x):
         # print('----entry shape', x.shape, '---\n')
+        x = self.dropout_weak(x)
         x = self.cv1(x)
         x = self.upsample(x)
         # x = self.relu(x)
         # print('----upsample shape', x.shape, '---\n')
+        x = self.dropout_normal(x)
         x = self.cv2(x)
         x = self.upsample(x)
+
+        x = self.dropout_normal(x)
         # x = self.relu(x)
         x = self.cv3(x)
         # print('----out shape', x.shape, '---\n')

train.py

@@ -536,7 +536,7 @@ def parse_opt(known=False):
 
 
 def main(opt, callbacks=Callbacks()):
-    print('\n---------- VERSION:', '#0012', '----------\n')
+    print('\n---------- VERSION:', '#0013', '----------\n')
     # Checks
     if RANK in {-1, 0}:
         print_args(vars(opt))