Universe

Hard Hat Workers Computer Vision Dataset

byNortheastern University - ChinaTask:
Object Detection
License:Public Domain
See all 7k imagesHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing head, helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing head, helmetHard Hat Workers Workers sample showing head, helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmetHard Hat Workers Workers sample showing helmet

About Hard Hat Workers 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.

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 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.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify 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 | Roboflow Train

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

Use Free Head, Helmet and Person Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "head, helmet, person"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicensePublic Domain

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

@misc{ hard-hat-workers-dp7ei_dataset,
  title = { Hard Hat Workers Dataset },
  type = { Open Source Dataset },
  author = { Northeastern University - China },
  howpublished = { \url{ https://universe.roboflow.com/cs-workspace-gkdyk/hard-hat-workers-dp7ei } },
  url = { https://universe.roboflow.com/cs-workspace-gkdyk/hard-hat-workers-dp7ei },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2026 },
  month = { may },
  note = { visited on 2026-07-29 },
}

Similar Projects

See More