机械技术支持东莞网站建设,wordpress微信分享按钮,青岛模板做网站,泽库县公司网站建设YOLO_V8分割
YOLO安装
pip install ultralytics
YOLO的数据集转化看csdn
数据标注EIseg EIseg这块#xff0c;正常安装就好#xff0c;但是numpy和各类包都容易有冲突#xff0c;python版本装第一点 数据标注过程中#xff0c;记得把JSON和COCO都点上#xff0c;把自…YOLO_V8分割
YOLO安装
pip install ultralytics
YOLO的数据集转化看csdn
数据标注EIseg EIseg这块正常安装就好但是numpy和各类包都容易有冲突python版本装第一点 数据标注过程中记得把JSON和COCO都点上把自动保存点上如果标注后需要修改记得手动点右下角保存否则“空格”没有存上。 预训练模型在/home/gsh/Projects/Yanbao/dataset/static_hrnet18_ocr64_cocolvis.zip
格式转化
labelme转COCO paddle标注得到的是labelme格式文件首先由于是多人标注其中label和imagePath是不一样的所以需要统一该格式采用read_json_save_all.py脚本修改。在/home/gsh/Projects/Yanbao/dataset/data_for_train_2/read_json_save_all.py该脚本生成的是每个图片对应的labelme.json文件。需要输入所有标注的labelme文件夹路径输出保存路径和图片路径。 labelme转COCO采用的是官方脚本labelme-json2labelme-coco.py 在~/Projects/PaddleSeg/EISeg/tool下
python labelme-json2labelme-coco.py [path to json dir] [path to 输出/output] --labels [path to labels.txt]
# 注意上述的json dir要用统一后的json文件
# 例如
python /home/gsh/Projects/PaddleSeg/EISeg/tool/labelme-json2labelme-coco.py /home/gsh/Projects/Yanbao/dataset/data_for_train_2/all_data1/tongyi_data /home/gsh/Projects/Yanbao/dataset/output --labels /home/gsh/Projects/Yanbao/dataset/data_for_train_2/all_data1/together/label/labelme/labels.txt结果如下
├── annotations.json
├── JPEGImages
└── VisualizationCOCO转YOLOV8
将上面生成的annotations.json进行处理将JPEGImages替换为.建议方法是VSCode打开后进行全局查找替换。 JSON2YOLO库中 general_json2yolo.py 在~/Projects/JSON2YOLO下 修改一下这里的路径注意这里要将上面的annotations.json放到一个文件夹中然后将文件夹路径输入 cls91to80也改成false 把这里的-1去掉这样类编号就从0开始了
默认生成的路径是执行代码的路径下生成new_dir文件夹
也可以改路径在代码的第258行
生成的是这样的 ├── images └── labels └── annotations
调整为这样 ├── images │ ├── train │ └── val └── labels ├── train └── val
至此数据处理全部结束
YOLO-seg的训练 代码在/home/gsh/Projects/YOLOV8/segtrain.py其中model YOLO(/home/gsh/Downloads/yolov8l-seg.pt)这里面的名字写的谁要么读取本地的要么直接从网上下写什么下什么 记得修改yaml中的文件夹路径和类别代码在/home/gsh/Projects/ultralytics/ultralytics/cfg/datasets/coco8-seg.yaml train的选择参数很多参考这里 results model.train(data/home/gsh/Projects/ultralytics/ultralytics/cfg/datasets/coco8-seg.yaml, epochs1000,imgsz640, batch-1)整体例子 # Load a model
# model YOLO(yolov8l-seg.yaml) # build a new model from YAML
model YOLO(/home/gsh/Downloads/yolov8s-seg.pt) # load a pretrained model (recommended for training)
# model YOLO(/home/gsh/Downloads/yolov8l-seg.pt) # load a pretrained model (recommended for training)
# model YOLO(yolov8l-seg.yaml).load(/home/gsh/Downloads/yolov8l-seg.pt) # build from YAML and transfer weights
# model YOLO(yolov8l-seg.yaml).load(/home/gsh/Downloads/yolov8l-seg.pt) # build from YAML and transfer weights
# Train the model
results model.train(data/home/gsh/Projects/ultralytics/ultralytics/cfg/datasets/coco8-seg.yaml, epochs900,imgsz640 ,batch32)YOLO-seg的预测
predict
from ultralytics import YOLO# Load a pretrained YOLOv8n model
model YOLO(yolov8n.pt)# Run inference on an image
results model(bus.jpg) # results list# View results
for r in results:print(r.boxes) # print the Boxes object containing the detection bounding boxesYOLO-Seg部署模型转化
onnx的部署
X64平台
模型转化代码
from ultralytics import YOLOmodelYOLO(/home/gsh/Projects/YOLOV8/runs/segment/train8/weights/best.pt)
model.export(formatonnx, device0, int8True)报错
Loading /home/gsh/Projects/YOLOV8/runs/segment/train8/weights/best.onnx for ONNX Runtime inference… [1;31m2024-08-26 09:30:46.347354781 [E:onnxruntime:Default, provider_bridge_ort.cc:1992 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1637 onnxruntime::Provider onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn.so.9: cannot open shared object file: No such file or directory [m [0;93m2024-08-26 09:30:46.347381605 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:965 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they’re in the PATH, and that your GPU is supported.[m
推理
直接替换掉模型路径即可
Jetson AGX
改名字
best.pt改成best-seg.pt我也不知道为什么要加seg据说是它模型转化要认这是个什么任务
模型转化代码
from ultralytics import YOLOmodelYOLO(/home/gsh/Projects/YOLOV8/runs/segment/train8/weights/best.pt)
model.export(formatonnx, imgsz(480,640), device0, int8True, simplifyTrue, task segment)Tensort的部署
官网介绍
模型转化代码
from ultralytics import YOLOmodel YOLO(yolov8n.pt)
model.export(formatengine,dynamicTrue, batch1, workspace10, int8True,datacoco.yaml, # 这个yaml文件要用自己训练的yaml文件
)# Load the exported TensorRT INT8 model
model YOLO(yolov8n.engine, taskdetect)# Run inference
result model.predict(https://ultralytics.com/images/bus.jpg)推理
直接替换掉模型路径即可
