droplets Object Detection Dataset and Pre-Trained Model by wanglianggithub

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Explore Dataset

Here are a few use cases for this project:

Plumbing Repairs and Maintenance: The model could be used to detect and classifying droplet leakages in pipes, aiding in early detection of potential plumbing faults. Detecting crossing or merging droplets could indicate abnormal water flow patterns suggestive of pipeline issues.
Quality Control in Manufacturing Processes: In industries where droplets are used in production processes (like pharmaceutical, paint, or chemical industries), the model could be used to classify droplet behaviors - whether droplets are being dispensed correctly or if they're crossing, indicating potential defects or deviations in the production process.
Weather Research and Predictions: The model could be employed to study droplet formations and patterns on surfaces to understand their correlations with different weather phenomena. The "crossing" class could also indicate rain or mist situations that can be empirical data for weather analysis and predictions.
Medical Diagnostics: The model might assist in the analysis of biomedical samples, classifying different patterns of droplet formation or migration that could be indicative of certain diseases or conditions.
Agriculture and Irrigation: The model could be useful to optimize irrigation systems by understanding droplet dispersion patterns (including "crossing") and thereby minimizing water waste while maximizing crop water absorption.

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{
                            droplets-oa7e9_dataset,
                            title = { droplets Dataset },
                            type = { Open Source Dataset },
                            author = { wanglianggithub },
                            howpublished = { \url{ https://universe.roboflow.com/wanglianggithub/droplets-oa7e9 } },
                            url = { https://universe.roboflow.com/wanglianggithub/droplets-oa7e9 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-05-12 },
                            }