Explicitly set `weight_decay` value (#8592)

train.py

@@ -163,12 +163,12 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     if opt.optimizer == 'Adam':
         optimizer = Adam(g[2], lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
     elif opt.optimizer == 'AdamW':
-        optimizer = AdamW(g[2], lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
+        optimizer = AdamW(g[2], lr=hyp['lr0'], betas=(hyp['momentum'], 0.999), weight_decay=0.0)
     else:
         optimizer = SGD(g[2], lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
 
     optimizer.add_param_group({'params': g[0], 'weight_decay': hyp['weight_decay']})  # add g0 with weight_decay
-    optimizer.add_param_group({'params': g[1]})  # add g1 (BatchNorm2d weights)
+    optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
     LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
                 f"{len(g[1])} weight (no decay), {len(g[0])} weight, {len(g[2])} bias")
     del g