neogpx/constructionfaqm

Dataset Labels

['bull_dozer', 'dumb_truck', 'excavator', 'grader', 'loader', 'mobile_crane', 'roller']

Number of Images

{'valid': 1271, 'test': 636, 'train': 4431}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("neogpx/constructionfaqm", name="full")
example = ds['train'][0]

Roboflow Dataset Page

[https://universe.roboflow.com/kfu-ye4kz/heavy_equipment-ifaqm/dataset/2 ](https://universe.roboflow.com/kfu-ye4kz/heavy_equipment-ifaqm/dataset/2 ?ref=roboflow2huggingface)

Citation

@misc{
                            heavy_equipment-ifaqm_dataset,
                            title = { Heavy_Equipment Dataset },
                            type = { Open Source Dataset },
                            author = { KFU },
                            howpublished = { \\url{ https://universe.roboflow.com/kfu-ye4kz/heavy_equipment-ifaqm } },
                            url = { https://universe.roboflow.com/kfu-ye4kz/heavy_equipment-ifaqm },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jul },
                            note = { visited on 2025-02-11 },
                            }

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on January 17, 2023 at 4:38 AM GMT

Roboflow is an end-to-end computer vision platform that helps you

• collaborate with your team on computer vision projects
• collect & organize images
• understand and search unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 6338 images. Heavy-equipment are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)
• Resize to 640x640 (Stretch)

No image augmentation techniques were applied.