只训练COCO数据集中的car和person两类

stillbetter commented 2 months ago

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你好，我的目标是检测人和车，所以请问如果我直接使用coco数据集，如何当dataloader只输出person和car的标签和图像进行训练，而不会把其他类别的bbox输出。

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glenn-jocher commented 2 months ago

@stillbetter 你好！

要在COCO数据集中只训练car和person两类，你可以通过以下步骤来实现：

创建自定义数据集配置文件：首先，创建一个新的dataset.yaml文件，只包含你感兴趣的类别。在这个例子中，我们只需要car和person两类。

# dataset.yaml
path: ../datasets/coco  # 数据集根目录
train: images/train2017  # 训练图像路径
val: images/val2017  # 验证图像路径

# Classes
names:
  0: person
  1: car

过滤标签：你需要过滤COCO数据集中的标签，只保留car和person的标签。你可以使用以下脚本来实现这一点：

import os
import shutil

# 定义你感兴趣的类别
target_classes = [0, 2]  # person 和 car 在 COCO 中的类别索引

# 定义路径
dataset_path = '../datasets/coco'
train_labels_path = os.path.join(dataset_path, 'labels/train2017')
val_labels_path = os.path.join(dataset_path, 'labels/val2017')

def filter_labels(labels_path):
    for label_file in os.listdir(labels_path):
        label_path = os.path.join(labels_path, label_file)
        with open(label_path, 'r') as f:
            lines = f.readlines()

        filtered_lines = [line for line in lines if int(line.split()[0]) in target_classes]

        if filtered_lines:
            with open(label_path, 'w') as f:
                f.writelines(filtered_lines)
        else:
            os.remove(label_path)

# 过滤训练和验证标签
filter_labels(train_labels_path)
filter_labels(val_labels_path)

训练模型：使用你创建的dataset.yaml文件来训练模型。

python train.py --img 640 --epochs 3 --data dataset.yaml --weights yolov5s.pt

这样，你的模型将只使用car和person两类进行训练。如果你在训练过程中遇到任何问题，请确保你使用的是最新版本的YOLOv5，并且所有依赖项都已正确安装。

希望这能帮助你实现目标！如果有其他问题，请随时提问 😊

祝你好运！

ultralytics / yolov5