Detection Object Detection Dataset and Pre-Trained Model by NIT

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318 images

Explore Dataset

Here are a few use cases for this project:

Geology and Mineralogy Research: The "Detection" model can be used by geologists and mineralogists to analyze and differentiate between various types of spattering events during volcanic eruptions, helping identify rock and mineral formations.
Environmental and Volcanic Monitoring: This model can assist in monitoring and predicting the intensity of volcanic activities by detecting and measuring spattering patterns from past or ongoing events, thus providing valuable data for risk assessment and hazard prevention.
Art and Design: The "Detection" model can be utilized by artists and designers to study spattering patterns and textures as an inspiration for new creations or to replicate natural effects in their work.
Forensic Investigations: Law enforcement and forensic experts can employ the "Detection" model to analyze evidence involving spattering patterns, such as blood spatters in crime scenes, to reconstruct events and support case-solving.
Quality Control in Manufacturing: Industries that involve spattering processes as part of their production methods (e.g., paint, coatings, or ceramics) can benefit from the "Detection" model in real-time monitoring and control of spattering consistency and quality, ensuring product uniformity and minimizing defects.

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{
                            detection-a1jtk_dataset,
                            title = { Detection Dataset },
                            type = { Open Source Dataset },
                            author = { NIT },
                            howpublished = { \url{ https://universe.roboflow.com/nit-y97vf/detection-a1jtk } },
                            url = { https://universe.roboflow.com/nit-y97vf/detection-a1jtk },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { nov },
                            note = { visited on 2024-06-26 },
                            }