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yolov11 epoch100轮训练笔记5 kaggle comet

web 2025/5/6 9:43:33

Football Players Detection using YOLOV11 | Kaggle

!pip install comet_ml


import comet_mlcomet_ml.login(project_name="c")

Comet - Build Better Models Faster

yolov11训练

100轮一眨眼训练完了

然而comet接不到yolo的sdk

优秀

训练17轮map就0.99了

v5训练100轮才0.6

有了，就是训练轮次不够

效果和11比差远了

map在50轮后才稳定上升

有边框信息了

本地detect读取边界框信息

w torch==2.5.1 torchvision --index-url https://download.pytorch.org/whl/cu124
w ultralytics

~~windows下torch能要我命睡个午觉先~~

from ultralytics import YOLO
import numpy as np
# 加载模型
model = YOLO('./best.pt')# 执行预测并获取结果
results = model.predict(source='./valid/images/screenshot-20250504103355_png.rf.71febf531b60b1e416aece6136e28388.jpg')# 遍历结果
for result in results:boxes = result.boxes  # 获取所有检测到的边框for box in boxes:print("BoundingBox: ", box.xyxy)  # 输出边框坐标(x1, y1, x2, y2)print("Confidence: ", box.conf)   # 置信度分数print("Class: ", box.cls)         # 类别IDprint("物体是: ",model.names[int(box.cls.item()) ],"范围为: ",box.xyxy.cpu().numpy())

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