Hard Hat Sample Computer Vision Project

0

views

0

downloads
Description

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.

Example Image: 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 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.

About Roboflow

Roboflow 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.

Roboflow Workmark

Supervision

Build Computer Vision Applications Faster with Supervision

Visualize and process your model results with our reusable computer vision tools.

Cite This Project

LICENSE
Public Domain

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            hard-hat-sample-lqhlr_dataset,
                            title = { Hard Hat Sample Dataset },
                            type = { Open Source Dataset },
                            author = { Northeastern University - China },
                            howpublished = { \url{ https://universe.roboflow.com/dwarf-tomato/hard-hat-sample-lqhlr } },
                            url = { https://universe.roboflow.com/dwarf-tomato/hard-hat-sample-lqhlr },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { oct },
                            note = { visited on 2024-12-24 },
                            }