YOLOv8¶
约 678 个字 171 行代码 预计阅读时间 4 分钟
yolov8官方github仓库
ultralytics/README.zh-CN.md at main · ultralytics/ultralytics · GitHub
yolov8官方使用文档
Home - Ultralytics YOLOv8 Docs
1¶
(Windows)RuntimeError解决方案
2¶
RuntimeError: Dataset 'data\Helmet.yaml' error
Dataset 'data\Helmet.yaml' images not found , missing paths ['C:\\Users\\Administrator\\datasets\\data\\datasets\\Helmet\\val_list.txt']
Note dataset download directory is 'C:\Users\Administrator\datasets'. You can update this in 'C:\Users\Administrator\AppData\Roaming\Ultralytics\settings.yaml'
修改，
可以改成相对路径
3¶
RuntimeError:
Attempt to start a new process before the current process
has finished its bootstrapping phase.
This probably means that you are on Windows and you have
forgotten to use the proper idiom in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce a Windows executable.
操作：
model = YOLO("yolov8n.pt") # 加载预训练模型（建议用于训练）
if __name__ == '__main__': # 添加__main__
# 使用模型
model.train(data="data/Helmet.yaml", epochs=3, batch=2[, name="Name"]) # 训练模型
4¶
MemoryError 调整 batch=
About Memory Error while Training yolov8x-seg · Issue #2916 · ultralytics/ultralytics (github.com)
5¶
页面大小不够：
OSError: [WinError 1455] The paging file is too small for this operation to complete. Error loading "...\cusolver64_xx.dll ...\cudnn_adv_infer64_8.dll" or one of its dependencies.
新建文件 fixNvPe.py
Python Script to disable ASLR and make nv fatbins read-only to reduce memory commit (github.com)
fixNvPe.py
# Simple script to disable ASLR and make .nv_fatb sections read-only
# Requires: pefile ( python -m pip install pefile )
# Usage: fixNvPe.py --input path/to/*.dll
import argparse
import pefile
import glob
import os
import shutil
def main(args):
failures = []
for file in glob.glob( args.input, recursive=args.recursive ):
print(f"\n---\nChecking {file}...")
pe = pefile.PE(file, fast_load=True)
nvbSect = [ section for section in pe.sections if section.Name.decode().startswith(".nv_fatb")]
if len(nvbSect) == 1:
sect = nvbSect[0]
size = sect.Misc_VirtualSize
aslr = pe.OPTIONAL_HEADER.IMAGE_DLLCHARACTERISTICS_DYNAMIC_BASE
writable = 0 != ( sect.Characteristics & pefile.SECTION_CHARACTERISTICS['IMAGE_SCN_MEM_WRITE'] )
print(f"Found NV FatBin! Size: {size/1024/1024:0.2f}MB ASLR: {aslr} Writable: {writable}")
if (writable or aslr) and size > 0:
print("- Modifying DLL")
if args.backup:
bakFile = f"{file}_bak"
print(f"- Backing up [{file}] -> [{bakFile}]")
if os.path.exists( bakFile ):
print( f"- Warning: Backup file already exists ({bakFile}), not modifying file! Delete the 'bak' to allow modification")
failures.append( file )
continue
try:
shutil.copy2( file, bakFile)
except Exception as e:
print( f"- Failed to create backup! [{str(e)}], not modifying file!")
failures.append( file )
continue
# Disable ASLR for DLL, and disable writing for section
pe.OPTIONAL_HEADER.DllCharacteristics &= ~pefile.DLL_CHARACTERISTICS['IMAGE_DLLCHARACTERISTICS_DYNAMIC_BASE']
sect.Characteristics = sect.Characteristics & ~pefile.SECTION_CHARACTERISTICS['IMAGE_SCN_MEM_WRITE']
try:
newFile = f"{file}_mod"
print( f"- Writing modified DLL to [{newFile}]")
pe.write( newFile )
pe.close()
print( f"- Moving modified DLL to [{file}]")
os.remove( file )
shutil.move( newFile, file )
except Exception as e:
print( f"- Failed to write modified DLL! [{str(e)}]")
failures.append( file )
continue
print("\n\nDone!")
if len(failures) > 0:
print("***WARNING**** These files needed modification but failed: ")
for failure in failures:
print( f" - {failure}")
def parseArgs():
parser = argparse.ArgumentParser( description="Disable ASLR and make .nv_fatb sections read-only", formatter_class=argparse.ArgumentDefaultsHelpFormatter )
parser.add_argument('--input', help="Glob to parse", default="*.dll")
parser.add_argument('--backup', help="Backup modified files", default=True, required=False)
parser.add_argument('--recursive', '-r', default=False, action='store_true', help="Recurse into subdirectories")
return parser.parse_args()
###############################
# program entry point
#
if __name__ == "__main__":
args = parseArgs()
main( args )
无依赖安装依赖
运行命令行
6¶
用命令行检测
代码在：
Python - Ultralytics YOLOv8 Docs
- 调用相机
source="0" - 检测整个文件夹的图片/视频，
source=".../folder" - 检测单个图片/视频（可以直接写路径，或者用类加载，三种方法）
show=展示图片save=保存图片(到runs/detect/[predictxx]文件夹中)name=保存的文件夹的名称
from ultralytics import YOLO
from PIL import Image
import cv2
model = YOLO("model.pt")
# accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam
results = model.predict(source="0")
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments
# from PIL
im1 = Image.open("bus.jpg")
results = model.predict(source=im1, save=True) # save plotted images
# from ndarray
im2 = cv2.imread("bus.jpg")
results = model.predict(source=im2, save=True, save_txt=True) # save predictions as labels
# from list of PIL/ndarray
results = model.predict(source=[im1, im2])
支持的文件类型
Predict - Ultralytics YOLOv8 Docs
参数的作用
Predict - Ultralytics YOLOv8 Docs
7¶
.yaml文件里面的标签要与labels.txt里面的顺序一致
8¶
v8 .yaml 文件示例：
# 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/coco8 # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images
test: # test images (optional)
# Classes (80 COCO classes)
names:
0: person
1: bicycle
2: car
...
77: teddy bear
78: hair drier
79: toothbrush
Object Detection Datasets Overview - Ultralytics YOLOv8 Docs
其中 train: val: 之后的内容有三种写法，
- 文件夹路径
- txt文件路径，txt文件内为各个图片的路径
- 列表形式的图片路径
9¶
如果detect时使用某个pt模型报错，有可能是因为 训练该模型时使用的ultralytics版本 比 本机安装的版本 新，使用了旧版本 requirements.txt 中未安装的包，因此更新ultralytics包即可
10¶
train时要修改batch，修改 batch 参数： batch=x
默认为16，-1为自动调整
Configuration - Ultralytics YOLOv8 Docs
11¶
...
File "E:\Anaconda3\envs\Pytorch\Lib\site-packages\ultralytics\engine\trainer.py", line 537, in save_metrics
with open(self.csv, 'a') as f:
^^^^^^^^^^^^^^^^^^^
PermissionError: [Errno 13] Permission denied: 'run\\detect\\data1st2\\results.csv'
进程已结束，退出代码为 1
不知什么原因，报错原因应该是 result.csv 文件被打开被占用，但我并没有打开这个文件。
重新再运行程序即可
Training fails when results.csv file is open · Issue #862 · ultralytics/ultralytics (github.com)
12¶
...
File "E:\Anaconda3\envs\Pytorch\Lib\site-packages\ultralytics\utils\tal.py", line 152, in get_box_metrics
bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt] # b, max_num_obj, h*w
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported ar some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
进程已结束，退出代码为 1
...
大多数情况下，CUDA运行时错误可能是某些索引不匹配的原因，例如您尝试在具有 10 个标签的数据集上训练具有15个输出节点的网络。这个 CUDA 错误的事情是，一旦你收到这个错误一次，你就会在使用 torch.tensors 执行的每个操作中收到它。这会强制您重新启动笔记本。
...
而后经过检查发现，是由于 labels.txt 文件中只有11个标签，而标签文件中有 11 (第12个标签)
python - Pytorch fails with CUDA error: device-side assert triggered on Colab - Stack Overflow
13¶
恢复/继续之前的训练
Train - Ultralytics YOLOv8 Docs
(可以新建一个 resume_train.py 文件)
from ultralytics import YOLO
if __name__ == '__main__':
# Load a model
model = YOLO('runs/detect/data1st9/weights/last.pt') # load a partially trained model
# Resume training
results = model.train(resume=True)
最后更新:
2023-10-24
创建日期: 2023-07-11
创建日期: 2023-07-11