Porosity-consolidated images Object Detection Dataset and Pre-Trained Model by Porosity

Images

6817 images

Explore Dataset

Here are a few use cases for this project:

Material Quality Inspection: The "Porosity-consolidated images" model could be used in manufacturing and construction industries to predict the quality of materials. By identifying the porosity classes, the model can offer vital insights about the strength, durability, and longevity of various materials.
Geological Exploration: This model could be useful for geological studies, specifically for analyzing different rock formations. It would help in determining the porosity of the rocks, which can provide important information about the subsurface environment.
Petroleum Industry: For petroleum engineers, understanding the porosity of the rock formations is crucial to accurately determine the amount of oil or gas that can be extracted. The model could help optimize the extraction process.
Civil Engineering Projects: The model can be used in civil engineering projects such as tunneling or bridge construction, where knowing the porosity of the materials might affect the calculated load-bearing capacity.
Educational Purposes: The computer vision model could serve as an effective educational tool for students studying material science, civil engineering, and geology. It would help them understand porosity types and their implications on different structures.

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{
                            porosity-consolidated-images_dataset,
                            title = { Porosity-consolidated images Dataset },
                            type = { Open Source Dataset },
                            author = { Porosity },
                            howpublished = { \url{ https://universe.roboflow.com/porosity/porosity-consolidated-images } },
                            url = { https://universe.roboflow.com/porosity/porosity-consolidated-images },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-13 },
                            }