深度学习（yolo11训练）

 

 

这里记录一下用yolo11训练coco数据集。

1、下载coco数据集：https://cocodataset.org/#download，下载这三个文件即可：2017 Train images [118K/18GB]，2017 Val images [5K/1GB]，2017 Train/Val annotations [241MB]。

2、使用下面脚本将coco数据集label转为yolo能识别地格式，json_path和label_path改为自己的目录，val和train分别执行一次。

import os
import json
from tqdm import tqdm
import argparse

# 生成训练集标签（换成自己的路径）
json_path = '/home/tc/Downloads/coco/annotations_trainval2017/annotations/instances_val2017.json'
label_path = '/home/tc/Downloads/coco/label/val'

parser = argparse.ArgumentParser()
parser.add_argument('--json_path', default=json_path, type=str,
                    help="input: coco format(json)")
parser.add_argument('--save_path', default=label_path, type=str,
                    help="specify where to save the output dir of labels")
arg = parser.parse_args()


def convert(size, box):
    dw = 1. / (size[0])
    dh = 1. / (size[1])
    x = box[0] + box[2] / 2.0
    y = box[1] + box[3] / 2.0
    w = box[2]
    h = box[3]

    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)


if __name__ == '__main__':
    json_file = arg.json_path  # COCO Object Instance 类型的标注
    ana_txt_save_path = arg.save_path  # 保存的路径

    data = json.load(open(json_file, 'r'))
    if not os.path.exists(ana_txt_save_path):
        os.makedirs(ana_txt_save_path)

    print(list(data))
    id_map = {}
    for i, category in enumerate(data['categories']):
        id_map[category['id']] = i

    # 通过事先建表来降低时间复杂度
    max_id = 0
    for img in data['images']:
        max_id = max(max_id, img['id'])

    img_ann_dict = [[] for i in range(max_id + 1)]
    for i, ann in enumerate(data['annotations']):
        img_ann_dict[ann['image_id']].append(i)

    for img in tqdm(data['images']):
        filename = img["file_name"]
        img_width = img["width"]
        img_height = img["height"]
        img_id = img["id"]
        head, tail = os.path.splitext(filename)
        ana_txt_name = head + ".txt"
        f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
        for ann_id in img_ann_dict[img_id]:
            ann = data['annotations'][ann_id]
            box = convert((img_width, img_height), ann["bbox"])
            f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
        f_txt.close()

3、使用下面脚本将val和train的文件名导出为单独的文件val.txt和train.txt，同样是val和train各执行一次。

import os

# 指定要读取的文件夹路径
folder_path = "/home/tc/Downloads/coco/images/val2017"  # 替换为你的文件夹路径
output_file = "val.txt"  # 存储结果的txt文件

# 获取文件夹中所有文件的绝对路径
file_paths = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]

# 将文件路径按行写入txt文件
with open(output_file, "w") as f:
    for file_path in file_paths:
        f.write(file_path + "\n")

print(f"文件路径已存储在 {output_file} 中。")

 4、配置yolo训练yaml文件，根据自己的路径修改配置，文件是yolo工程自带的。

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# COCO 2017 dataset https://cocodataset.org by Microsoft
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
# Example usage: yolo train data=coco.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco  ← downloads here (20.1 GB)

# 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: /home/tc/Downloads/coco # dataset root dir
train: train2017.txt # train images (relative to 'path') 118287 images
val: val2017.txt # val images (relative to 'path') 5000 images
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush

5、执行训练脚本：

from ultralytics import YOLO
import multiprocessing

if __name__ == "__main__":
    multiprocessing.freeze_support()
    model = YOLO("yolo11l.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="coco.yaml", epochs=10, imgsz=640)

6、如果一切顺利，就可以等训练结束，训练后在./runs/detect/train/下有中间结果和模型文件，下面是中间生成的一些结果：