FileNotFoundError: train: D:\yolo\datasets\coco128\datasets\coco128\images\train does not exist

FileNotFoundError是一个Python异常，表示文件或目录不存在。在你提供的错误信息中，报告了一个文件不存在的错误，具体是train文件夹在路径D:\yolo\datasets\coco128\datasets\coco128\images\train下不存在。

可能的原因是：
1. 路径错误：请确保路径中的文件夹和文件名正确，并且没有拼写错误。
2. 文件或文件夹被移动或删除：如果你之前有这个文件或文件夹，但现在已经被移动或删除了，那么就会出现该错误。
3. 权限问题：如果你没有足够的权限来访问该文件或文件夹，也会导致该错误。

请检查以上可能的原因，并确保路径和文件名正确，并且文件或文件夹存在。如果问题仍然存在，请提供更多的信息，以便我能够更好地帮助你解决问题。

相关问题

AssertionError: train: No labels found in D:\YOLO\datasets\Armor\labels\1.cache, can not start training.

这个错误提示表明在训练YOLO模型时，找不到训练数据集中的标签文件。解决这个问题的方法如下：

1. 确认标签文件是否存在，标签文件应该与图像文件在同一目录下，且文件名相同，只是扩展名不同。标签文件的扩展名通常为.txt，每个文件应包含与其对应的图像文件中所有对象的标签信息。

2. 确认标签文件的格式是否正确。YOLO模型要求标签文件的格式为每行一个对象，每行包含对象的类别和位置信息。位置信息应该是相对于图像宽度和高度的归一化坐标，即左上角和右下角的坐标值应该在0到1之间。

3. 确认训练脚本中的数据集路径和标签文件路径是否正确。如果数据集路径或标签文件路径不正确，就会导致找不到标签文件的错误。

4. 修改datasets.py文件。在该文件中，需要将标签文件的路径替换为正确的路径。具体来说，需要将datasets.py文件中的JPEGImages替换为标签文件所在的目录。

以下是修改后的datasets.py文件的示例代码：

import glob
import os

import numpy as np
import torch
from PIL import Image

from torch.utils.data import Dataset


class LoadImagesAndLabels(Dataset):  # for training/testing
    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
                 cache_images=False, single_cls=False):
        path = str(Path(path))  # os-agnostic
        assert os.path.isfile(path), f'File not found {path}'
        with open(path, 'r') as f:
            self.img_files = [x.replace('\n', '') for x in f.readlines() if os.path.isfile(x.replace('\n', ''))]
        assert self.img_files, f'No images found in {path}'
        self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
                            .replace('.jpeg', '.txt') for x in self.img_files]
        self.img_size = img_size
        self.batch_size = batch_size
        self.augment = augment
        self.hyp = hyp
        self.rect = rect
        self.image_weights = image_weights
        self.cache_images = cache_images
        self.single_cls = single_cls

    def __len__(self):
        return len(self.img_files)

    def __getitem__(self, index):
        img_path = self.img_files[index % len(self.img_files)].rstrip()
        label_path = self.label_files[index % len(self.img_files)].rstrip()

        # Load image
        img = None
        if self.cache_images:  # option 1 - caches small/medium images
            img = self.imgs[index % len(self.imgs)]
        if img is None:  # option 2 - loads large images on-the-fly
            img = Image.open(img_path).convert('RGB')
            if self.cache_images:
                if img.size[0] < 640 or img.size[1] < 640:  # if one side is < 640
                    img = img.resize((640, 640))  # resize
                self.imgs[index % len(self.imgs)] = img  # save
        assert img.size[0] > 9, f'Width must be >9 pixels {img_path}'
        assert img.size[1] > 9, f'Height must be >9 pixels {img_path}'

        # Load labels
        targets = None
        if os.path.isfile(label_path):
            with open(label_path, 'r') as f:
                x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
            # Normalized xywh to pixel xyxy format
            labels = x.copy()
            if x.size > 0:
                labels[:, 1] = x[:, 1] * img.width  # xmin
                labels[:, 2] = x[:, 2] * img.height  # ymin
                labels[:, 3] = x[:, 3] * img.width  # xmax
                labels[:, 4] = x[:, 4] * img.height  # ymax
                labels[:, 1:5] = xywh2xyxy(labels[:, 1:5])  # xywh to xyxy
                targets = torch.zeros((len(labels), 6))
                targets[:, 1:] = torch.from_numpy(labels)

        # Apply augmentations
        if self.augment:
            img, targets = random_affine(img, targets,
                                         degrees=self.hyp['degrees'],
                                         translate=self.hyp['translate'],
                                         scale=self.hyp['scale'],
                                         shear=self.hyp['shear'],
                                         border=self.img_size // 2)  # border to remove

        # Letterbox
        img, ratio, pad = letterbox(img, new_shape=self.img_size, auto=self.rect, scaleup=self.augment,
                                    stride=self.hyp['stride'])
        targets[:, 2:6] = xyxy2xywh(targets[:, 2:6]) / self.img_size / ratio  # normalized xywh (to grid cell)

        # Load into tensor
        img = np.array(img).transpose(2, 0, 1)  # HWC to CHW
        img = torch.from_numpy(img).to(torch.float32)  # uint8 to fp16/32
        targets = targets[torch.where(targets[:, 0] == index % len(self.img_files))]  # filter by image index
        return img, targets, index, img_path

    def coco_index(self, index):
        """Map dataset index to COCO index (minus 1)"""
        return int(Path(self.img_files[index]).stem) - 1

    @staticmethod
    def collate_fn(batch):
        img, label, _, path = zip(*batch)  # transposed
        for i, l in enumerate(label):
            l[:, 0] = i  # add target image index for build_targets()
        return torch.stack(img, 0), torch.cat(label, 0), path


class LoadImages(Dataset):  # for inference
    def __init__(self, path, img_size=640, stride=32, auto=True):
        path = str(Path(path))  # os-agnostic
        if os.path.isdir(path):
            files = sorted(glob.glob('%s/*.*' % path))
        elif os.path.isfile(path):
            files = [path]
        else:
            raise Exception(f'Error: {path} does not exist')
        images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
        videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
        ni, nv = len(images), len(videos)

        self.img_size = img_size
        self.stride = stride
        self.auto = auto
        self.video_flag = [False] * ni + [True] * nv
        self.img_files = images + videos
        self.cap = [cv2.VideoCapture(x) for x in videos]
        self.frame = [None] * nv
        self.ret = [False] * nv
        self.path = path

    def __len__(self):
        return len(self.img_files)

    def __getitem__(self, index):
        if self.video_flag[index]:
            return self.load_video(index)
        else:
            return self.load_image(index)

    def load_image(self, index):
        img_path = self.img_files[index]
        img = cv2.imread(img_path)  # BGR
        assert img is not None, 'Image Not Found ' + img_path
        h0, w0 = img.shape[:2]  # orig hw
        img = letterbox(img, new_shape=self.img_size, auto=self.auto)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)

        return torch.from_numpy(img), index, img_path, (h0, w0)

    def load_video(self, index):
        cap = self.cap[index]
        while True:
            self.ret[index], frame = cap.read()
            if not self.ret[index]:
                break
            if self.frame[index] is None:
                self.frame[index] = letterbox(frame, new_shape=self.img_size, auto=self.auto)[0]
                self.frame[index] = self.frame[index][:, :, ::-1].transpose(2, 0, 1)
                self.frame[index] = np.ascontiguousarray(self.frame[index])
            else:
                self.frame[index] = torch.cat((self.frame[index][self.stride:], letterbox(frame, new_shape=self.img_size,
                                                                                          auto=self.auto)[0]), 0)
            if self.ret[index]:
                return self.frame[index], index, self.img_files[index], frame.shape[:2]

    def __del__(self):
        if hasattr(self, 'cap'):
            for c in self.cap:
                c.release()


def letterbox(img, new_shape=640, color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
    # Resize and pad image while meeting stride-multiple constraints
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        ratio = float(new_shape) / max(shape)
    else:
        ratio = min(float(new_shape[0]) / shape[0], float(new_shape[1]) / shape[1])
    if ratio != 1:  # always resize down, only resize up if shape < new_shape * 1.5
        if scaleup or (ratio < 1 and max(shape) * ratio > stride * 1.5):
            interp = cv2.INTER_LINEAR
            if ratio < 1:
                img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
            else:
                img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
        else:
            interp = cv2.INTER_AREA
            img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
    new_shape = [round(shape[1] * ratio), round(shape[0] * ratio)]

    # Compute stride-aligned boxes
    if auto:
        stride = int(np.ceil(new_shape[0] / stride) * stride)
        top_pad = (stride - new_shape[0]) % stride  # add top-padding (integer pixels only)
        left_pad = (stride - new_shape[1]) % stride  # add left-padding (integer pixels only)
        if top_pad or left_pad:
            img = cv2.copyMakeBorder(img, top_pad // 2, top_pad - top_pad // 2, left_pad // 2, left_pad - left_pad // 2,
                                     cv2.BORDER_CONSTANT, value=color)  # add border
    else:
        stride = 32
        top_pad, left_pad = 0, 0

    # Pad to rectangular shape divisible by stride
    h, w = img.shape[:2]
    if scaleFill or new_shape == (w, h):  # scale-up width and height
        new_img = np.zeros((new_shape[1], new_shape[0], 3), dtype=np.uint8) + color  # whole image
        nh, nw = h, w
    else:  # scale width OR height
        nh = new_shape[1] - top_pad
        nw = new_shape[0] - left_pad
        assert nh > 0 and nw > 0, 'image size < new_size'
        new_img = np.zeros((new_shape[1], new_shape[0], 3), dtype=np.uint8) + color  # whole image
        if nw / w <= nh / h:  # resize by width, then pad height
            new_w = new_shape[0]
            new_h = int(nh * new_w / nw)
            assert new_h > 0, 'image size < new_size'
            img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
            top = top_pad // 2
            bottom = top_pad - top
            left = left_pad // 2
            right = left_pad - left
            new_img[top:top + new_h, left:left + new_w] = img
        else:  # resize by height, then pad width
            new_h = new_shape[1]
            new_w = int(nw * new_h / nh)
            assert new_w > 0, 'image size < new_size'
            img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
            top = top_pad // 2
            bottom = top_pad - top
            left = left_pad // 2
            right = left_pad - left
            new_img[top:top + new_h, left:left + new_w] = img

    return new_img, ratio, (top_pad, left_pad)


def xywh2xyxy(x):
    # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
    y = x.copy() if isinstance(x, np.ndarray) else np.array(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y


def xyxy2xywh(x):
    # Convert bounding

(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\yolov8s4 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

根据你提供的错误信息，可以看出文件路径'C:\Users\as729/ultralytics/ultralytics/datasets/new.yaml'是无法找到的。

请注意，路径中的斜杠应该是正斜杠（/），而不是反斜杠（\）。确保在路径中使用正确的斜杠。

此外，你还可以尝试使用原始的绝对路径'C:\Users\as729\ultralytics\ultralytics\datasets\new.yaml'，并确保该文件实际上存在于该位置。

如果问题仍然存在，建议检查文件是否存在，并确保路径正确无误。如果问题仍然无法解决，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。