更改最小测试示例

federated_learning/test_data/client1/train/labels/img1.txt

@@ -1 +0,0 @@
-0 0.5 0.5 0.2 0.2

federated_learning/test_data/client1/train/labels/img2.txt

@@ -1 +0,0 @@
-1 0.3 0.3 0.4 0.4

federated_learning/test_data/client1/val/labels/img1.txt

@@ -1 +0,0 @@
-0 0.5 0.5 0.2 0.2

federated_learning/test_data/client1/val/labels/img2.txt

@@ -1 +0,0 @@
-1 0.3 0.3 0.4 0.4

federated_learning/test_data/client2/train/labels/img1.txt

@@ -1 +0,0 @@
-0 0.5 0.5 0.2 0.2

federated_learning/test_data/client2/train/labels/img2.txt

@@ -1 +0,0 @@
-1 0.3 0.3 0.4 0.4

federated_learning/test_data/client2/val/labels/img1.txt

@@ -1 +0,0 @@
-0 0.5 0.5 0.2 0.2

federated_learning/test_data/client2/val/labels/img2.txt

@@ -1 +0,0 @@
-1 0.3 0.3 0.4 0.4

federated_learning/yolov8_fed.py

@@ -71,7 +71,7 @@ def federated_train(num_rounds, clients_data):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     global_model = YOLO("yolov8n.pt").to(device)
     # 设置类别数
-    global_model.model.nc = 2
+    # global_model.model.nc = 2
     
     for _ in range(num_rounds):
         client_weights = []
@@ -96,7 +96,7 @@ def federated_train(num_rounds, clients_data):
             local_model.train(
                 data=data_path,
                 epochs=1,  # 每轮本地训练1个epoch
-                imgsz=128,  # 图像大小
+                imgsz=640,  # 图像大小
                 verbose=False  # 关闭冗余输出
             )