Hard Hat Workers Object Detection Dataset - raw_75-25_trainTestSplit

# 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

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

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## 概述

“安全帽”数据集是一份针对要求佩戴安全帽的职场环境下工作人员的目标检测数据集。该数据集的标注还包括仅包含“人员”和“头部”的示例，以应对个别个体可能不佩戴安全帽的情况出现。

原始数据集采用[75/25的训练-测试分割](https://blog.roboflow.com/train-test-split/)。

示例图像：

## 应用场景

可以利用此数据集构建一个分类器，用于识别在职场中遵守安全规范的工人与可能未遵守规范的工人。此外，它也是一个极佳的通用数据集，适用于实践操作。

## 使用此数据集

通过点击“分支”或“下载此数据集”按钮，可以将此数据集复制到您的Roboflow账户，并使用新的预处理设置（例如，根据模型所需格式调整大小或转换为灰度图），或添加额外的增强操作以提高模型泛化能力。该数据集特别适合Roboflow的新高级[仅边界框增强](https://blog.roboflow.ai/introducing-bounding-box-level-augmentations/)。

## 数据集版本（https://help.roboflow.com/workspaces-projects-and-versions）

[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)：使用原始的75/25训练-测试分割生成，未应用增强
* **“v2” (raw_75-25_trainTestSplit)**：使用原始的75/25训练-测试分割生成，**这些是原始图像**
* “v3” (v3)：使用原始的75/25训练-测试分割生成，使用修改类别删除“人员”类别，应用预处理和增强
* “v5” (raw_HeadHelmetClasses)：使用70/20/10的训练/验证/测试分割生成，使用修改类别删除“人员”类别
* “v8” (raw_HelmetClassOnly)：使用70/20/10的训练/验证/测试分割生成，使用修改类别删除“头部”和“人员”类别
* “v9” (raw_PersonClassOnly)：使用70/20/10的训练/验证/测试分割生成，使用修改类别删除“头部”和“安全帽”类别
* **“v10” (raw_AllClasses)**：使用70/20/10的训练/验证/测试分割生成，**这些是原始图像**
* **“v11” (augmented3x-AllClasses-FastModel)**：使用70/20/10的训练/验证/测试分割生成，应用预处理和增强，3倍图像生成，*使用Roboflow的Fast Model训练*
* **“v12” (augmented3x-HeadHelmetClasses-FastModel)**：使用70/20/10的训练/验证/测试分割生成，应用预处理和增强，修改类别用于删除“人员”类别，3倍图像生成，*使用Roboflow的Fast Model训练*
* **“v13” (augmented3x-HeadHelmetClasses-AccurateModel)**：使用70/20/10的训练/验证/测试分割生成，应用预处理和增强，修改类别用于删除“人员”类别，3倍图像生成，*使用Roboflow的Accurate Model训练*
* “v14” (raw_HeadClassOnly)：使用70/20/10的训练/验证/测试分割生成，使用修改类别删除“人员”类别，并将“安全帽”类别重映射/重新标记为“头部”

[计算机视觉模型大小选择](https://blog.roboflow.com/computer-vision-model-tradeoff/) | [Roboflow 训练](https://docs.roboflow.com/train)

## 关于Roboflow

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