Hard Hat Workers Object Detection Dataset

# Overview

The `Hard Hat` dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a [75/25 train-test split](https://blog.roboflow.com/train-test-split/).

Example Image:

# Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

# Using this Dataset

Use the `fork` or `Download this Dataset` button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced [Bounding Box Only Augmentations](https://blog.roboflow.ai/introducing-bounding-box-level-augmentations/).

## Dataset [Versions](https://help.roboflow.com/workspaces-projects-and-versions):
[Image Preprocessing](https://docs.roboflow.com/image-transformations/image-preprocessing) | [Image Augmentation](https://docs.roboflow.com/image-transformations/image-augmentation) | [Modify Classes](https://help.roboflow.com/modifying-classes)
* `v1` (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations
* **`v2` (raw_75-25_trainTestSplit)**: generated with the original 75/25 train-test split | **These are the raw, original images**
* `v3` (v3): generated with the original 75/25 train-test split | Modify Classes used to drop `person` class | Preprocessing and Augmentation applied
* `v5` (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `person` class
* `v8` (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `head` and `person` classes
* `v9` (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `head` and `helmet` classes
* **`v10` (raw_AllClasses)**: generated with a 70/20/10 train/valid/test split | **These are the raw, original images**
* **`v11` (augmented3x-AllClasses-FastModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | *Trained with Roboflow's Fast Model*
* **`v12` (augmented3x-HeadHelmetClasses-FastModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop `person` class | 3x image generation | *Trained with Roboflow's Fast Model*
* **`v13` (augmented3x-HeadHelmetClasses-AccurateModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop `person` class | 3x image generation | *Trained with Roboflow's Accurate Model*
* `v14` (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `person` class, and remap/relabel `helmet` class to `head`

[Choosing Between Computer Vision Model Sizes](https://blog.roboflow.com/computer-vision-model-tradeoff/) | [Roboflow Train](https://docs.roboflow.com/train)

# About Roboflow

[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

#### [](https://roboflow.ai)

{'# Overview': '概述', 'The "Hard Hat" dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.': '“安全帽”数据集为一项目标检测数据集，收录了需佩戴安全帽的工作场所中的工人图像。标注亦包含仅涉及“人员”与“头部”的示例，以应对个体可能未佩戴安全帽的情况。', 'The original dataset has a [75/25 train-test split](https://blog.roboflow.com/train-test-split/).': '原始数据集采用75/25的训练-测试分割比例。', 'Example Image: ': '示例图像：', '# Use Cases': '应用场景', 'One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.': '本数据集可用于构建分类器，以区分遵守工作场所安全规范的工人与可能违反规范者。此外，它亦是练习之良选。', '# Using this Dataset': '使用此数据集', "Use the `fork` or `Download this Dataset` button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced [Bounding Box Only Augmentations](https://blog.roboflow.ai/introducing-bounding-box-level-augmentations/).": '通过点击“fork”或“下载此数据集”按钮，可将本数据集复制至您的Roboflow账户，并使用新的预处理设置进行导出（可能包括调整至模型所需的格式或转换为灰度图），或添加额外的增强操作以提高模型泛化能力。本数据集尤其适用于Roboflow最新推出的高级[仅边界框增强](https://blog.roboflow.ai/introducing-bounding-box-level-augmentations/)功能。', '# Dataset [Versions](https://help.roboflow.com/workspaces-projects-and-versions):': '## 数据集版本', '[Image Preprocessing](https://docs.roboflow.com/image-transformations/image-preprocessing) | [Image Augmentation](https://docs.roboflow.com/image-transformations/image-augmentation) | [Modify Classes](https://help.roboflow.com/modifying-classes)': '[图像预处理](https://docs.roboflow.com/image-transformations/image-preprocessing) | [图像增强](https://docs.roboflow.com/image-transformations/image-augmentation) | [修改类别](https://help.roboflow.com/modifying-classes)', '* `v1` (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations': '* 版本v1（调整至416x416并反射）：基于原始75/25的训练-测试分割生成，未进行增强操作', '* **`v2` (raw_75-25_trainTestSplit)**: generated with the original 75/25 train-test split | **These are the raw, original images**': '* **版本v2（raw_75-25_trainTestSplit）**：基于原始75/25的训练-测试分割生成，**这些为原始图像**', '`v3` (v3): generated with the original 75/25 train-test split | Modify Classes used to drop `person` class | Preprocessing and Augmentation applied': '版本v3（v3）：基于原始75/25的训练-测试分割生成，使用修改类别操作删除“人员”类别，并应用预处理和增强操作', '`v5` (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `person` class': '版本v5（raw_HeadHelmetClasses）：基于70/20/10的训练/验证/测试分割生成，使用修改类别操作删除“人员”类别', '`v8` (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `head` and `person` classes': '版本v8（raw_HelmetClassOnly）：基于70/20/10的训练/验证/测试分割生成，使用修改类别操作删除“头部”和“人员”类别', '`v9` (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `head` and `helmet` classes': '版本v9（raw_PersonClassOnly）：基于70/20/10的训练/验证/测试分割生成，使用修改类别操作删除“头部”和“安全帽”类别', '`v10` (raw_AllClasses)**: generated with a 70/20/10 train/valid/test split | **These are the raw, original images**': '版本v10（raw_AllClasses）：基于70/20/10的训练/验证/测试分割生成，**这些为原始图像**', "`v11` (augmented3x-AllClasses-FastModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | *Trained with Roboflow's Fast Model*": '版本v11（augmented3x-AllClasses-FastModel）：基于70/20/10的训练/验证/测试分割生成，应用预处理和增强操作，生成3倍图像，*使用Roboflow快速模型训练*', "`v12` (augmented3x-HeadHelmetClasses-FastModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop `person` class | 3x image generation | *Trained with Roboflow's Fast Model*": '版本v12（augmented3x-HeadHelmetClasses-FastModel）：基于70/20/10的训练/验证/测试分割生成，应用预处理和增强操作，使用修改类别操作删除“人员”类别，生成3倍图像，*使用Roboflow快速模型训练*', "`v13` (augmented3x-HeadHelmetClasses-AccurateModel)**: generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop `person` class | 3x image generation | *Trained with Roboflow's Accurate Model*": '版本v13（augmented3x-HeadHelmetClasses-AccurateModel）：基于70/20/10的训练/验证/测试分割生成，应用预处理和增强操作，使用修改类别操作删除“人员”类别，生成3倍图像，*使用Roboflow精确模型训练*', '`v14` (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop `person` class, and remap/relabel `helmet` class to `head`': '版本v14（raw_HeadClassOnly）：基于70/20/10的训练/验证/测试分割生成，使用修改类别操作删除“人员”类别，并将“安全帽”类别重新映射/重新标记为“头部”', '[Choosing Between Computer Vision Model Sizes](https://blog.roboflow.com/computer-vision-model-tradeoff/) | [Roboflow Train](https://docs.roboflow.com/train)': '[在计算机视觉模型大小之间进行选择](https://blog.roboflow.com/computer-vision-model-tradeoff/) | [Roboflow训练](https://docs.roboflow.com/train)', '# About Roboflow': '关于Roboflow', '[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.': 'Roboflow使计算机视觉数据集的管理、预处理、增强和版本管理变得无缝。', "Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.": '开发者在使用Roboflow工作流程时，可减少50%的代码量，自动化标注质量保证，节省训练时间，并提高模型的可复现性。', '#### [](https://roboflow.ai)': '#### [](https://roboflow.ai)'}

搜集汇总

背景与挑战

背景概述

该数据集是一个包含7041张图片的物体检测数据集，专注于工作场所中工人佩戴安全帽的检测，同时包含'人'和'头'的标注。数据集提供多个版本，包括原始图像和经过预处理、增强的版本，适用于不同的计算机视觉模型训练需求。