使用离火插件yoloV8数据标注，模型训练

1. 启动

 

2.相关配置 

2.1    data.yaml

path: D:/yolo-tool/yaunshen-yolov8/YOLOv8ys/YOLOv8-CUDA10.2/1/datasets/ceshi001
train: images
val: images
names: ['蔡徐坤','篮球']

2.2   cfg.yaml

# Ultralytics YOLOv8, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training

task: detect  # inference task, i.e. detect, segment, classify
mode: train  # YOLO mode, i.e. train, val, predict, export

# Train settings -------------------------------------------------------------------------------------------------------
model: C:\Users\AF5\Desktop\YOLOv8ql\YOLOv8-CPU\1\datasets\qh\pt\train2\weights\best.pt  # path to model file, i.e. yolov8n.pt, yolov8n.yaml    模型文件路径
data: C:\Users\AF5\Desktop\YOLOv8ql\YOLOv8-CPU\1\datasets\qh\data.yaml  # path to data file, i.e. i.e. coco128.yaml    数据集data文件路径
epochs: 100000  # number of epochs to train for    训练次数，达到这个次数后将终止训练，且无法该模型无法继续训练
patience: 0  # epochs to wait for no observable improvement for early stopping of training    超过这个次数没有提升将自动完成训练
batch: 1  # number of images per batch (-1 for AutoBatch)    批数量，设越大占用显存越多
imgsz: 640  # size of input images as integer or w,h    一般默认640，训练时的图片宽高
save: True  # save train checkpoints and predict results
save_period: -1  # Save checkpoint every x epochs (disabled if < 1)
cache: False  # True/ram, disk or False. Use cache for data loading
device:  # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 0  # number of worker threads for data loading (per RANK if DDP)    勿改，必须为0
project: C:/Users/AF5/Desktop/YOLOv8ql/YOLOv8-CPU/1/datasets/qh/val  # project name    勿改
name: train  # experiment name    训练完成的文件夹名称
exist_ok: False  # whether to overwrite existing experiment
pretrained: False  # whether to use a pretrained model
optimizer: SGD  # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: True  # whether to print verbose output
seed: 0  # random seed for reproducibility
deterministic: True  # whether to enable deterministic mode
single_cls: False  # train multi-class data as single-class
image_weights: False  # use weighted image selection for training
rect: False  # support rectangular training if mode='train', support rectangular evaluation if mode='val'
cos_lr: False  # use cosine learning rate scheduler
close_mosaic: 10  # disable mosaic augmentation for final 10 epochs
resume: False  # resume training from last checkpoint    为True时为继续模型的训练
min_memory: False  # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>]
# Segmentation
overlap_mask: True  # masks should overlap during training (segment train only)
mask_ratio: 4  # mask downsample ratio (segment train only)
# Classification
dropout: 0.0  # use dropout regularization (classify train only)

# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True  # validate/test during training    为True，训练时计算mAP
split: val  # dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False  # save results to JSON file
save_hybrid: False  # save hybrid version of labels (labels + additional predictions)
conf:   # object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7  # intersection over union (IoU) threshold for NMS
max_det: 300  # maximum number of detections per image
half: False  # use half precision (FP16)
dnn: False  # use OpenCV DNN for ONNX inference
plots: True  # save plots during train/val

# Prediction settings --------------------------------------------------------------------------------------------------
source: C:\Users\AF5\Desktop\YOLOv8ql\YOLOv8-CPU\1\datasets\qh\images\qh174.png  # source directory for images or videos    需要进行预测视频或图片的路径
show: False  # show results if possible
save_txt: True  # save results as .txt file
save_conf: False  # save results with confidence scores
save_crop: False  # save cropped images with results
hide_labels: False  # hide labels
hide_conf: False  # hide confidence scores
vid_stride: 1  # video frame-rate stride
line_thickness: 3  # bounding box thickness (pixels)
visualize: False  # visualize model features
augment: False  # apply image augmentation to prediction sources
agnostic_nms: False  # class-agnostic NMS
classes:  # filter results by class, i.e. class=0, or class=[0,2,3]
retina_masks: False  # use high-resolution segmentation masks
boxes: True  # Show boxes in segmentation predictions

# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript  # format to export to
keras: False  # use Keras
optimize: False  # TorchScript: optimize for mobile
int8: False  # CoreML/TF INT8 quantization
dynamic: False  # ONNX/TF/TensorRT: dynamic axes
simplify: False  # ONNX: simplify model
opset: 12  # ONNX: opset version (optional)
workspace: 4  # TensorRT: workspace size (GB)
nms: False  # CoreML: add NMS

# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01  # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01  # final learning rate (lr0 * lrf)
momentum: 0.937  # SGD momentum/Adam beta1
weight_decay: 0.0005  # optimizer weight decay 5e-4
warmup_epochs: 3.0  # warmup epochs (fractions ok)
warmup_momentum: 0.8  # warmup initial momentum
warmup_bias_lr: 0.1  # warmup initial bias lr
box: 7.5  # box loss gain
cls: 0.5  # cls loss gain (scale with pixels)
dfl: 1.5  # dfl loss gain
fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
label_smoothing: 0.0  # label smoothing (fraction)
nbs: 64  # nominal batch size
hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4  # image HSV-Value augmentation (fraction)
degrees: 0.0  # image rotation (+/- deg)
translate: 0.1  # image translation (+/- fraction)
scale: 0.5  # image scale (+/- gain)
shear: 0.0  # image shear (+/- deg)
perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
flipud: 0.0  # image flip up-down (probability)
fliplr: 0.5  # image flip left-right (probability)
mosaic: 1.0  # image mosaic (probability)
mixup: 0.0  # image mixup (probability)
copy_paste: 0.0  # segment copy-paste (probability)

# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg:  # for overriding defaults.yaml

# Debug, do not modify -------------------------------------------------------------------------------------------------
v5loader: False  # use legacy YOLOv5 dataloader

# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml  # tracker type, ['botsort.yaml', 'bytetrack.yaml']

2.3 主要代码

import cv2
import time
from ultralytics import YOLO
import json
import numpy as np

def Yolov10Detector(frame, model, image_size, conf_threshold, cap):
    results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
    frame = results[0].plot()

    # 获取当前帧的时间
    current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000  # 以秒为单位

    # 打印所有标签结果及对应的时间
    for result in results:
        for box in result.boxes:
            c = int(box.cls)
            name = result.names[c]
            print(f"识别到的标签: {name}，对应的时间: {current_time} 秒")
    return frame




def main():
    image_size = 640  # Adjust as needed
    conf_threshold = 0.3  # Adjust as needed
    model = YOLO("D:/yolo-workspace/yoloy8-project/model/oneself/best.pt")
    source = "C:/Users/wangwei/Desktop/2024-09-18/20240925_115452.mp4"  # 0 for webcam
    cap = cv2.VideoCapture(source)

    while True:
        success, frame = cap.read()
        start_time = time.time()

        if success:
            print("读取帧成功！")
        if not success:
            print("读取帧失败！")
            break

        modelName = model.names
        json.dumps(modelName, ensure_ascii=False)
        #print("预检测 识别转json  信息为：" + json.dumps(modelName, ensure_ascii=False))
        frame = Yolov10Detector(frame, model, image_size, conf_threshold, cap)
        end_time = time.time()
        fps = 1 / (end_time - start_time)
        framefps = "FPS:{:.2f}".format(fps)

        try:
            cv2.rectangle(frame, (10, 1), (120, 20), (0, 0, 0), -1)
            cv2.putText(frame, framefps, (15, 17), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
        except Exception as e:
            print("")
        cv2.imshow("yolov10-本地摄像头识别", frame)  # Display the annotated frame

        if cv2.waitKey(1) & 0xFF == ord('q'):  # Exit on 'q' key pres:
            break
    cap.release()
    cv2.destroyAllWindows()

main()

3. 模型训练

4.训练结果：

原文地址：https://blog.csdn.net/weixin_41037490/article/details/142518364

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