pCO2 Instance Segmentation Dataset and Pre-Trained Model by P Gschwe

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Explore Dataset

Here are a few use cases for this project:

Industrial Quality Control: This model could be used in manufacturing plants for quality assurance. Raw materials, like Carbon, Silver, or Fiber, that are difficult to distinguish could be accurately identified by the model, reducing defective products.
Medical Research: "pCO2" might find application in microscopy for medical or research purposes. For example, identifying specific particles in a sample, such as detecting abnormal cells or contaminants.
Material Engineering: The ability to distinguish complex materials like RuO2, PVP, and AgAgCl can assist researchers in material engineering during the process of material creation and in the analysis of their properties.
Textile Industry: The computer vision model could identify different types of fibers. It might support processes such as the separation of mixed fiber batches or the monitoring of the quality during the production process.
Pollution Monitoring: This model could help environmental scientists identify particulate matter in collected air or water samples, enabling more accurate pollution monitoring and helping lead to effective solutions.

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{
                            pco2-mnqnd_dataset,
                            title = { pCO2 Dataset },
                            type = { Open Source Dataset },
                            author = { P Gschwe },
                            howpublished = { \url{ https://universe.roboflow.com/p-gschwe/pco2-mnqnd } },
                            url = { https://universe.roboflow.com/p-gschwe/pco2-mnqnd },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-04-26 },
                            }