if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. 4. 基于自定义数据集训练模型 4.1 从零开始准备YOLO 自定义数据集 YOLO自定义数据集主要涉及数据集的准备、标注、划分、格式转换...
epochs=3)if__name__=='__main__':#如果在 Windows 上运行且需要多进程支持,保留以下行multiprocessing.freeze_support() main() 模型验证 当我们训练得到一个.pt文件后,可能需要对其进行评估以获取该.pt的指标,代码如下: fromultralyticsimportYOLO#--- 加载模型 ---model = YOLO('runs/detect/train3/wei...
The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. Y-T-G commented May 26, 2024 You have to add: if __name__ == '__main__': before your training script. 👍 1 Author Twilighthui commented May 26, 2024 Thanks ...
if name == ‘main‘: freeze_support() … The “freeze_support()” line can be omitted if the program is not going to be frozen to produce an executable. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 错误原因: Python 解释器在 Windows平台 执行创建多进程的程序时,子进程会读取当前 Pytho...
freeze_support() ... The"freeze_support()"line can be omittedifthe programisnotgoing to be frozen to produce an executable. 修改: """num_iter = 0 # vis = Visualizer() best_test_loss = np.inf for epoch in range(num_epochs):"""#修改后if__name__=='__main__': ...
from ultralytics import YOLO import torch import multiprocessing def main(): multiprocessing.freeze_support() # 只在 Windows 上需要 # Check if GPU is available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Training on {device}") # Load a model model =...
freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. Hmmm, I have seen this error in the case of multiprocessing. Not sure though. Well i have MX450 which could barely survive the training, so mostly i ...
freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. 传统方案经过查阅资料,发现上面错误对应的报错原因是,这是因为通常情况下,在linux系统中可以使用多个子进程加载数据,而在windows系统中不能。 出现问题的代码如下: import time from ultralytics...
freeze_support() # Load a model model = YOLO("yolo11s.pt") # load a pretrained model (recommended for training) and transfer weights device_str = "xpu:0" device = torch.device(device_str) model = model.to(device) # Train the model ...