Door-Windows Material Detection Computer Vision Project
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downloadsDoor-Windows Material Classification
Door-Windows Material Classification
About Dataset
This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai
This dataset is an extremely challenging set of over 3,000+ images of different material from multiple location. These images captured and crowdsourced from over 2000+ different locations, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. It contains a wide variety of different material images. This dataset can be used scene classification and domestic object detection.
Dataset Features
- Dataset size : 3000+ images
- Captured by : Over 2000+ crowdsource contributors
- Resolution : HD and above (1920x1080 and above)
- Location : Captured with 2000+ locations
- Diversity : Various lighting conditions like day, night, varied distances, view points etc.
- Device used : Captured using mobile phones in 2020-2022
- Usage : Image classification, domestic object detection, objects relationship understanding etc.
Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record
The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
door-windows-material-detection_dataset,
title = { Door-Windows Material Detection Dataset },
type = { Open Source Dataset },
author = { DataCluster Labs },
howpublished = { \url{ https://universe.roboflow.com/datacluster-labs-agryi/door-windows-material-detection } },
url = { https://universe.roboflow.com/datacluster-labs-agryi/door-windows-material-detection },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { nov },
note = { visited on 2024-11-21 },
}