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---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="neogpx/constructionfaqm" src="https://huggingface.co/datasets/neogpx/constructionfaqm/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['bull_dozer', 'dumb_truck', 'excavator', 'grader', 'loader', 'mobile_crane', 'roller']
```
### Number of Images
```json
{'valid': 1271, 'test': 636, 'train': 4431}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
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.
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