Namespace(weights='yolo7.pt', cfg='cfg/training/yolov7.yaml', data='data/DOTA_split.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=10, batch_size=4, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='', multi_scale=False, single_cls=False, ada m=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='exp', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs\\train\\exp2', total_batch_size=4) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw= 1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 Traceback (most recent call last): File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\train.py", line 618, in <module> train(hyp, opt, device, tb_writer) File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\train.py", line 64, in train data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\venv\lib\site-packages\yaml\__init__.py", line 79, in load loader = Loader(stream) File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\venv\lib\site-packages\yaml\loader.py", line 34, in __init__ Reader.__init__(self, stream) File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\venv\lib\site-packages\yaml\reader.py", line 85, in __init__ self.determine_encoding() File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\venv\lib\site-packages\yaml\reader.py", line 124, in determine_encoding self.update_raw() File "D:\Documents\Desktop\YOLO_suanfa\yolov7-main\venv\lib\site-packages\yaml\reader.py", line 178, in update_raw data = self.stream.read(size) UnicodeDecodeError: 'gbk' codec can't decode byte 0x80 in position 233: illegal multibyte sequence
时间: 2024-04-18 20:31:51 浏览: 49
这个错误是因为在读取 YAML 文件时使用了 GBK 编码,但在第233个位置出现了非法的多字节序列,导致无法解码。你可以尝试以下解决方法:
1. 将文件编码改为 UTF-8 或其他可支持的编码格式。
2. 使用适当的编码解码器来读取文件,例如使用 `utf-8` 编码器:
```python
import codecs
with codecs.open('cfg/training/yolov7.yaml', 'r', encoding='utf-8') as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader)
```
请确保你的代码中正确指定了文件的编码方式。
相关问题
torch.hub.load('ultralytics/yolov5', 'yolov5s')怎么改成本地使用
如果你已经下载了 YOLOv5 的代码和权重文件,你可以使用本地文件路径来加载模型,而不是从 Torch Hub 中下载:
```python
import torch
from models.yolo import Model
model = Model('path/to/weights.pt', cfg='path/to/model.yaml', device='cpu')
```
其中,`weights.pt` 是训练好的权重文件,`model.yaml` 是配置文件。你可以从 https://github.com/ultralytics/yolov5/releases 下载它们。
注意,如果你想在 GPU 上运行模型,需要将 `device` 参数更改为对应的 CUDA 设备编号,例如 `device='cuda:0'`。
另外,如果你想使用不同的 YOLOv5 模型,可以更改 `cfg` 参数为对应的配置文件路径,例如 `cfg='path/to/yolov5m.yaml'`。
(mypytorch) C:\Users\as729>yolo detect train data=C:/Users/as729/ultralytics/ultralytics/datasets/new.yaml model=C:/ultralytics/ultralytics/weights/yolov8s.pt epochs=150 imgsz=640 batch=16 patience=150 project=C:/ultralytics/runs/visdrone name=yolov8s Ultralytics YOLOv8.0.139 Python-3.9.17 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB) engine\trainer: task=detect, mode=train, model=C:/ultralytics/ultralytics/weights/yolov8s.pt, data=C:/Users/as729/ultralytics/ultralytics/datasets/new.yaml, epochs=150, patience=150, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=C:/ultralytics/runs/visdrone, name=yolov8s, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=C:\ultralytics\runs\visdrone\yolov8s5 Traceback (most recent call last): File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\engine\trainer.py", line 123, in __init__ self.data = check_det_dataset(self.args.data) File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\data\utils.py", line 196, in check_det_dataset data = check_file(dataset) File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\utils\checks.py", line 330, in check_file raise FileNotFoundError(f"'{file}' does not exist") FileNotFoundError: 'C:/Users/as729/ultralytics/ultralytics/datasets/new.yaml' does not exist The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Users\as729\.conda\envs\mypytorch\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "C:\Users\as729\.conda\envs\mypytorch\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "C:\Users\as729\.conda\envs\mypytorch\Scripts\yolo.exe\__main__.py", line 7, in <module> File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\cfg\__init__.py", line 410, in entrypoint getattr(model, mode)(**overrides) # default args from model File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\engine\model.py", line 367, in train self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks) File "C:\Users\as729\.conda\envs\mypytorch\lib\site-packages\ultralytics\engine\trainer.py", line 127, in __init__ raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e RuntimeError: Dataset 'C:\Users\as729\ultralytics\ultralytics\datasets\new.yaml' error 'C:/Users/as729/ultralytics/ultralytics/datasets/new.yaml' does not exist
如果你确定文件的绝对路径是正确的,但仍然找不到文件,有几个可能的原因:
1. 文件确实不存在:再次确认文件是否存在于指定的路径。你可以手动浏览到该路径,并验证文件是否存在。如果文件确实不存在,你需要创建或获取正确的文件。
2. 文件权限问题:确保你有足够的权限来访问该文件。在某些情况下,你可能需要以管理员身份运行程序或更改文件的权限设置。
3. 文件路径格式错误:确保文件路径中的斜杠是正斜杠(/),而不是反斜杠(\)。确保驱动器名和文件名正确拼写。
4. 工作目录问题:如果你使用相对路径,文件的位置相对于当前的工作目录。请确保程序的当前工作目录与你期望的一致。你可以使用`os.getcwd()`函数来获取当前工作目录,并使用`os.chdir()`函数来更改工作目录。
如果上述步骤都没有解决问题,请提供更多详细信息,例如操作系统、代码示例和完整的错误消息,以便我能够更好地帮助你解决问题。
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