YOLOv8训练报错FileNotFoundError: train: No labels found in ...VisDrone2019-DET-train/labels.cache

德彪稳坐倒骑驴

已于 2023-10-21 01:48:29 修改

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文章标签：深度学习人工智能机器学习目标检测视觉检测

于 2023-10-21 01:22:17 首次发布

本文链接：https://blog.youkuaiyun.com/Albert233333/article/details/133956144

版权

报错信息是什么？

我在使用YOLOv8，在VisDrone数据集上训练的时候，在使用train.,py进行数据下载和训练的时候，会遇到下面这个报错，导致无法正常训练。

train.py里面写的内容是下面这些

from ultralytics import YOLO
model = YOLO("yolov8n.yaml") 
model.train(data="VisDrone.yaml", epochs=1,batch=1)

完整的报错信息如下

  File "电脑路径/ultralytics/yolo/data/dataset.py", line 114, in get_labels
    raise FileNotFoundError(f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}")
FileNotFoundError: train: No labels found in /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache, can not start training. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data

更完整的报错信息

先是说，尝试进行了多次访问，都无法访问到那个下载链接

Dataset 'VisDrone.yaml' not found ⚠️, missing paths ['/root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-val/images']
Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train.zip...
Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-val.zip...
Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-test-dev.zip...
Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-test-challenge.zip...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 1/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip...
curl: (35) OpenSSL SSL_connect: SSL_ERROR_SYSCALL in connection to github.com:443 
⚠️ Download failure, retrying 2/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 1/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip...
Unzipping /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-val.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 1/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 2/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 1/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip...
curl: (7) Failed to connect to github.com port 443: Connection timed out
curl: (35) OpenSSL SSL_connect: SSL_ERROR_SYSCALL in connection to objects.githubusercontent.com:443 
⚠️ Download failure, retrying 2/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 3/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip...
Unzipping /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-test-challenge.zip to /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone...
curl: (56) Unexpected EOF
⚠️ Download failure, retrying 3/3 https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip...
curl: (56) Unexpected EOF

然后说找不到labels.cache 标签文件

train: Scanning /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels... 0 images, 6471 backgrounds, 0 corrup
train: WARNING ⚠️ No labels found in /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
train: New cache created: /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache
Traceback (most recent call last):
  File "train.py", line 3, in <module>
    model.train(data="VisDrone.yaml", epochs=1,batch=1)  
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/model.py", line 251, in train
    self.trainer.train()
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 187, in train
    self._do_train(RANK, world_size)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 252, in _do_train
    self._setup_train(rank, world_size)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 237, in _setup_train
    self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=rank, mode="train")
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/v8/detect/train.py", line 42, in get_dataloader
    build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/build.py", line 71, in build_dataloader
    dataset = YOLODataset(
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/dataset.py", line 44, in __init__
    super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/base.py", line 49, in __init__
    self.labels = self.get_labels()
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/dataset.py", line 114, in get_labels
    raise FileNotFoundError(f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}")
FileNotFoundError: train: No labels found in /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache, can not start training. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data

解释报错的原因

出现上面这种报错的情况下，你打开训练集验证集测试集的文件夹会发现下面这种情况。只有image图片和annnotations标注，但是没有labels这个文件夹

正常情况下应该是下面这样，训练集、验证集、测试集都是有 ultralytics加工过的labels文件夹和label.cache这个缓存文件。

labels这个文件夹,并不是天津大学VisDrone官方提供的。这个labels文件夹是你在运行上面train.py的这句话“model.train(data="VisDrone.yaml", epochs=1,batch=1) ”，执行了"VisDrone.yaml"里面 visdrone2yolo这个函数下面这些加工处理annotations文件夹中的标注数据后得到的

  def visdrone2yolo(dir):
      from PIL import Image
      from tqdm import tqdm

      def convert_box(size, box):
          # Convert VisDrone box to YOLO xywh box
          dw = 1. / size[0]
          dh = 1. / size[1]
          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh

      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
      for f in pbar:
          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
          lines = []
          with open(f, 'r') as file:  # read annotation.txt
              for row in [x.split(',') for x in file.read().strip().splitlines()]:
                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
                      continue
                  cls = int(row[5]) - 1
                  box = convert_box(img_size, tuple(map(int, row[:4])))
                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
                      fl.writelines(lines)  # write label.txt

但是因为在白天使用AutoDL这个平台的时候，下载的过程里，AutoDL连接外网十分不稳定，一会可以连接github外网，一会又连不上github，下载时断时续，导致VisDrone.yaml文件里面的下载步骤被中止。

下载一旦被迫中止，后续的原始数据进行加工的步骤自然也没来得及运行。

当时当你，此时再次运行train.py的时候，代码会识别你是否已经有几个下载下来的文件。只要有一个文件被下载下来，就不会继续之前的下载，最关键的是代码会同时自动跳过数据加工这个步骤。

此后无论你执行“python train.py”这句代码多少次，都不会把缺少的数据压缩包下载下来，同时也不会将 annotations文件夹进行再加工，也就没有labels这个文件夹，自然就会不停的报下面这个错，说找不到labels.cache这个文件。无论你执行“python train.py”这句代码多少次，都还是下面这个报错。我曾经为了这个报错，苦恼了一下午，怎么样debug都修不好。截图留念

train: Scanning /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache... 0 images, 6471 backgrounds, 0 
Traceback (most recent call last):
  File "train.py", line 3, in <module>
    model.train(data="VisDrone.yaml", epochs=1,batch=1)  
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/model.py", line 251, in train
    self.trainer.train()
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 187, in train
    self._do_train(RANK, world_size)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 252, in _do_train
    self._setup_train(rank, world_size)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/engine/trainer.py", line 237, in _setup_train
    self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=rank, mode="train")
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/v8/detect/train.py", line 42, in get_dataloader
    build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/build.py", line 71, in build_dataloader
    dataset = YOLODataset(
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/dataset.py", line 44, in __init__
    super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/base.py", line 49, in __init__
    self.labels = self.get_labels()
  File "/root/m_coding/YOLOv8n+LADHead/ultralytics/yolo/data/dataset.py", line 114, in get_labels
    raise FileNotFoundError(f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}")
FileNotFoundError: train: No labels found in /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train/labels.cache, can not start training. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data

总结来说，使用YOLOv8的时候，下载数据集就要一气呵成，中间不能出现任何网络中断。如果下载中断了，删除掉所有你已经下载好的所有文件，从头开始下载。多次下载，直到你碰到下载整个过程中网络通畅，一口气下载下来，然后让代码把数据加工完。

至于外网github的连接时断时续，有可能是因为，白天的时候有许多人通过AutoDL的这个外网VPN访问github，但是AutoDL提供的VPN的带宽有限，导致你分配到的下载带宽特别窄。所以你可以尝试在半夜的时候运行训练程序，把数据下载和加工好。

讲解如何解决这个报错

方向1：删除掉你已经下载的数据集，从头下载，直到可以流畅下载并加工annotations数据

这个方法是最有效、最高效、最切实可用的方法。

重复这个方法多次，直到你也能像我下面这样，可以把下面四个文件流畅的下载下来，并解压好

VisDrone2019-DET-test-challenge, VisDrone2019-DET-test-dev，VisDrone2019-DET-train，VisDrone2019-DET-val

你的terminal里面一定要有下面这三行，这三行表示，代码成功将“annotations”文件夹里面的数据加工成了YOLOv8指定的数据格式，形成了labels文件夹

Converting /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-train: 6471it [04:42, 22.88it/s] 
Converting /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-val: 548it [00:31, 17.59it/s]
Converting /root/m_coding/yolov3/datasets/VisDrone/datasets/VisDrone/VisDrone2019-DET-test-dev: 1610it [00:59, 27.17it/s]
Dataset download success ✅ (574.6s), saved to /root/m_coding/yolov3/datasets/VisDrone/datasets

方向2：拿现成的labels文件夹过来用

如果你之前保存了 traIn dev 和test三个数据的制作加工好的label文件夹和label.cache缓存文件。你可以直接放到这三个文件夹里面，放好以后是下面这样

train

val 验证集

test-dev 公开label的测试集

方向3：搞定下载的网络

问客服，为什么github的网络连接多次无法访问？要求他修复网络，保证下载数据过程中，与github之间的连接是稳定的

如果github的网络连接修复好了，保证下载的时候不间断，完整的把所有数据在一次运行的时候下载下来，

再运行一次，看看能不能正常制作label

方向4：将本地数据集上传，然后自己制作label文件夹

如果你电脑上，没有VisDrone 训练测试验证三个数据集的labels这个加工后的这个标注数据文件夹，你可以私信问我要。你也可以，绕开执行VisDrone.yaml这条途径，转而通过下面这个方法，自己制作出来。

通过这个路径“/ultralytics/yolo/data/datasets/VisDrone.yaml”可以找到YOLOv8的 VisDrone.yaml 数据集配置文件。

内容如下

# Ultralytics YOLO 🚀, GPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: python train.py --data VisDrone.yaml
# parent
# ├── yolov5
# └── datasets
#     └── VisDrone  ← downloads here (2.3 GB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/VisDrone  # dataset root dir
train: VisDrone2019-DET-train/images  # train images (relative to 'path')  6471 images
val: VisDrone2019-DET-val/images  # val images (relative to 'path')  548 images
test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images

# Classes
names:
  0: pedestrian
  1: people
  2: bicycle
  3: car
  4: van
  5: truck
  6: tricycle
  7: awning-tricycle
  8: bus
  9: motor


# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
  import os
  from pathlib import Path

  from ultralytics.yolo.utils.downloads import download

  def visdrone2yolo(dir):
      from PIL import Image
      from tqdm import tqdm

      def convert_box(size, box):
          # Convert VisDrone box to YOLO xywh box
          dw = 1. / size[0]
          dh = 1. / size[1]
          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh

      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
      for f in pbar:
          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
          lines = []
          with open(f, 'r') as file:  # read annotation.txt
              for row in [x.split(',') for x in file.read().strip().splitlines()]:
                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
                      continue
                  cls = int(row[5]) - 1
                  box = convert_box(img_size, tuple(map(int, row[:4])))
                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
                      fl.writelines(lines)  # write label.txt


  # Download
  dir = Path(yaml['path'])  # dataset root dir
  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
  download(urls, dir=dir, curl=True, threads=4)

  # Convert
  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

这里面写了数据集的下载链接。你可以先下载到本机

  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']

然后将这些压缩包上传到AutoDL的服务器上

然后你可以将VisDrone.yaml文件里，加工标注文件的这一部分放到一个 .py 文件，自行对annotation文件夹里标注数据进行加工

  def visdrone2yolo(dir):
      from PIL import Image
      from tqdm import tqdm

      def convert_box(size, box):
          # Convert VisDrone box to YOLO xywh box
          dw = 1. / size[0]
          dh = 1. / size[1]
          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh

      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
      for f in pbar:
          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
          lines = []
          with open(f, 'r') as file:  # read annotation.txt
              for row in [x.split(',') for x in file.read().strip().splitlines()]:
                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
                      continue
                  cls = int(row[5]) - 1
                  box = convert_box(img_size, tuple(map(int, row[:4])))
                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
                      fl.writelines(lines)  # write label.txt

这种方法我也没用过。只是提供思路给你。你可以试试，成功的可能性很高