Roboflow 宇宙包分割数据集

The Roboflow Package Segmentation Dataset is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.

该数据集包含一组多样化的图像，展示了不同背景和环境下的各种包装，是训练和评估分割模型的宝贵资源。无论您是从事物流、仓库自动化还是任何需要对包装进行精确分析的应用，包装分割数据集都能为您提供一套有针对性的综合图像，以提高计算机视觉算法的性能。

数据集结构

包装分割数据集的数据分布结构如下：

• 训练集：包含 1920 幅图像及其相应的注释。
• 测试集：由 89 幅图像组成，每幅图像都与各自的注释配对。
• 验证集：由 188 幅图像组成，每幅图像都有相应的注释。

应用

由包装分割数据集（Package Segmentation Dataset）推动的包装分割对于优化物流、加强最后一英里配送、改进制造质量控制以及促进智能城市解决方案至关重要。从电子商务到安全应用，该数据集是一项关键资源，促进了计算机视觉领域的创新，实现了多样化和高效的包装分析应用。

数据集 YAML

YAML（另一种标记语言）文件用于定义数据集配置。它包含数据集的路径、类和其他相关信息。以包分割数据集为例，YAML 文件中的 package-seg.yaml 文件保存在 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml.

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Package-seg dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
# Example usage: yolo train data=package-seg.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── package-seg  ← downloads here (102 MB)

# 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: ../datasets/package-seg # dataset root dir
train: images/train # train images (relative to 'path') 1920 images
val: images/val # val images (relative to 'path') 89 images
test: test/images # test images (relative to 'path') 188 images

# Classes
names:
  0: package

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/package-seg.zip

使用方法

To train Ultralytics YOLO11n model on the Package Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo segment train data=package-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

样本数据和注释

包裹分割数据集包括从多个角度拍摄的各种图像和视频。以下是数据集中的数据实例，并附有各自的注释：

• This image displays an instance of image object detection, featuring annotated bounding boxes with masks outlining recognized objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this task.
• 这个例子强调了 VisDrone 数据集的多样性和复杂性，突出了高质量传感器数据对于涉及无人机的计算机视觉任务的重要性。

引文和致谢

如果您将裂缝分割数据集纳入您的研究或开发计划，请引用以下论文：

@misc{ factory_package_dataset,
    title = { factory_package Dataset },
    type = { Open Source Dataset },
    author = { factorypackage },
    howpublished = { \url{ https://universe.roboflow.com/factorypackage/factory_package } },
    url = { https://universe.roboflow.com/factorypackage/factory_package },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2024 },
    month = { jan },
    note = { visited on 2024-01-24 },
}

我们对Roboflow 团队为创建和维护包裹分类数据集所做的努力表示感谢，该数据集是物流和研究项目的宝贵财富。有关包裹分类数据集及其创建者的更多详情，请访问包裹分类数据集页面。

常见问题

Roboflow 包裹分割数据集是什么？它对计算机视觉项目有何帮助？

Roboflow 软件包分割数据集是专为软件包分割任务量身定制的图片集。该数据集包含各种背景下的包装图像，对于训练和评估分割模型非常有价值。该数据集尤其适用于物流、仓库自动化和任何需要精确包装分析的项目。它有助于优化物流和增强视觉模型，从而实现准确的包装识别和分类。

How do I train an Ultralytics YOLO11 model on the Package Segmentation Dataset?

You can train an Ultralytics YOLO11n model using both Python and CLI methods. Use the snippets below:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.pt")  # load a pretrained model

# Train the model
results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo segment train data=package-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

更多详情，请参阅型号培训页面。

包装分类数据集由哪些部分组成，其结构如何？

The dataset is structured into three main components:

• Training set: Contains 1920 images with annotations.
• Testing set: Comprises 89 images with corresponding annotations.
• Validation set: Includes 188 images with annotations.

这种结构确保了数据集的平衡，以便进行全面的模型训练、验证和测试，从而提高分割算法的性能。

Why should I use Ultralytics YOLO11 with the Package Segmentation Dataset?

Ultralytics YOLO11 provides state-of-the-art accuracy and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLO11's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our page on YOLO11 segmentation.

如何访问和使用软件包分割数据集的 package-seg.yaml 文件？

"(《世界人权宣言》) package-seg.yaml 文件托管在Ultralytics' GitHub 存储库中，包含有关数据集路径、类和配置的基本信息。您可以从 这里.该文件对于配置模型以有效利用数据集至关重要。

如需了解更多见解和实际案例，请浏览我们的 "使用"部分。

📅 Created 9 months ago ✏️ Updated 23 days ago

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