【YOLO目标检测实战 】3.使用YOLO11训练COCO128数据集
1 训练YOLO11模型
- 准备训练数据
mkdir datasets && cd datasets
wget https://ultralytics.com/assets/coco128.zip
unzip coco128.zip
cd ..
- 准备预训练模型
mkdir weights && cd weights
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
cd ..
- 准备配置文件
# 准备数据配置文件
mkdir configs && cd configs
# 拷贝模型配置文件
cp path/to/ultralytics-8.3.24/ultralytics/cfg/models/11/yolo11.yaml ./yolo11.yaml
# 拷贝数据配置文件
cp path/to/ultralytics-8.3.24/ultralytics/cfg/datasets/coco128.yaml ./coco128.yaml
# 拷贝下载字体文件(字体文件在资料下载链接中下载)
cp path/to/Arial.ttf ~/.config/Ultralytics
cd ..
- 修改数据配置
vim configs/coco128.yaml
path: path/to/datasets/coco128 # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
test: # test images (optional)
- 训练YOLO11模型
mkdir scripts && cd scripts
vim scripts/train.py
from ultralytics import YOLO
model = YOLO('configs/yolo11s.yaml').load('weights/yolo11s.pt')
model.train(
epochs=100,
batch=8,
device='0',
workers=8,
imgsz=640,
project='runs',
amp=False,
cache='ram',# 'ram', 'disk' or False
data='configs/coco128.yaml'
)
python scripts/train.py
2 评估YOLO11模型
vim scripts/val.py
from ultralytics import YOLO
model = YOLO('runs/train/weights/best.pt')
metrics = model.val(project='runs')
metrics.box.map
metrics.box.map50
metrics.box.map75
metrics.box.maps
python scripts/val.py
3 YOLO11推理图片
vim scripts/predict.py
from ultralytics import YOLO
model = YOLO('runs/train/weights/best.pt')
results = model(
source='datasets/coco128/images/train2017',
imgsz=640,
conf=0.5,
save=True,
save_txt=True,
project='runs'
)
python scripts/predict.py
资料下载
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