Schraubenerkennung_NC Object Detection Dataset and Pre-Trained Model by Technische Universitaet Berlin

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Screw recognition and classification.

This project aims to generate a dataset for the recognition of screw heads. This project classifies the following 6 different types of screw heads:

phillips
pozidriv
hex
hex_socket
slot_drive
torx

For the creation of this record the following rules are considered:

Label around the entirety of an object. It is best to include a little bit of non-object buffer than it is to exclude a portion of the object with a rectangular label. So, aim to have boxes that tightly mirror the objects you want to label, but do not cut off part of the objects. Your model will understand edges far better this way.
For occluded objects, label them entirely. If an object is out of view due to another object being in front of it, label the object out of view as if you could see its entirety. Your model will begin to understand the true bounds of objects this way.
For objects partially out of frame, generally label them. This tip especially depends on your problem, but in general, even a partial object is still an object to be labeled.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite this Project

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

@misc{ schraubenerkennung_nc_dataset,
    title = { Schraubenerkennung_NC Dataset },
    type = { Open Source Dataset },
    author = { Technische Universitaet Berlin },
    howpublished = { \url{ https://universe.roboflow.com/technische-universitaet-berlin/schraubenerkennung_nc } },
    url = { https://universe.roboflow.com/technische-universitaet-berlin/schraubenerkennung_nc },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { oct },
    note = { visited on 2023-12-10 },
}