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快捷跑通ultralytics下的yolo系列

news 2025/7/13 7:59:37

快捷实现ultralytics下的yolo系列

1. 环境安装和配置
1.1 torch先行
根据电脑cuda版本安装适配torch，可以避免后续很多麻烦事情

nvcc -V   # 查看cuda版本

torch官网

https://pytorch.org/get-started/previous-versions/

例如我的11.6cuda，即可安装

pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116

1.2 安装ultralytics

Install the ultralytics package from PyPI

pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple/   # 加个清华源快到起飞

配置好两个文件，①数据集相关的描述文件

~/ultralytics/ultralytics/cfg/datasets/coco8-seg.yaml

#yoloseg_dataset/
#├── images/
#│   ├── train/
#│   │   ├── 0001.png ← 注意 file_name 中包含 train/000xxx.png
#│   └── val/
#│       ├── 0002.png← 注意 file_name 中包含 val/000xxx.png
#├── labels/
#│   ├── train/
#│   │   ├── 0001.txt
#│   └── val/
#│       ├── 0002.txt
# 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: /media/ly/AddDisk/InstanceSeg2025/yoloseg_dataset/ # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 imagestest: # test images (optional)task: segment  # 实例分割
segments: True # 启用 COCO分割nc: 1names:0: apple

②模型体积相关的描述文件

~/ultralytics/ultralytics/cfg/models/11/yolo11s-seg.yaml

# Parameters
nc: 1 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'# [depth, width, max_channels]
#  n: [0.50, 0.25, 1024] # summary: 203 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPss: [0.50, 0.50, 1024] # summary: 203 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
#  m: [0.50, 1.00, 512] # summary: 253 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
#  l: [1.00, 1.00, 512] # summary: 379 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
#  x: [1.00, 1.50, 512] # summary: 379 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)

开始训练(实例分割为例）

yolo task=segment mode=train \model=path/to/ultralytics/cfg/models/11/yolo11s-seg.yaml \data=path/to/ultralytics/ultralytics/cfg/datasets/coco8-seg.yaml \epochs=100 imgsz=640 batch=2