修改参数，使其符合训练数据集

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 = 1
     
     for _ in range(num_rounds):
         client_weights = []
@@ -86,7 +86,8 @@ def federated_train(num_rounds, clients_data):
             img_dir = os.path.join(yaml_dir, config.get('train', data_path))  # 从配置文件中获取图像目录
             
             print(f"Image directory: {img_dir}")
-            num_samples = len(glob.glob(os.path.join(img_dir, '*.jpg')))
+            num_samples = (len(glob.glob(os.path.join(img_dir, '*.jpg'))) +
+                           len(glob.glob(os.path.join(img_dir, '*.png'))))
             print(f"Number of images: {num_samples}")
             
             # 克隆全局模型
@@ -127,5 +128,5 @@ if __name__ == "__main__":
     # assert Path("yolov8n_federated.onnx").exists(), "模型导出失败"
     
     # 检查2：验证预测功能
-    # results = final_model.predict("test_data/client1/train/images/img1.jpg")
+    # results = final_model.predict("../dataset/val/images/VS_P65.jpg", save=True)
     # assert len(results[0].boxes) > 0, "预测结果异常"