fish Object Detection Dataset and Pre-Trained Model by Korea Univ

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Explore Dataset

Here are a few use cases for this project:

Fish market inventory management: This computer vision model can be used to streamline and automate the process of identifying and tracking fish species at fish markets or wholesale markets, reducing manual labor and improving the speed and accuracy of inventory management.
Educational and research purposes: Students, researchers, and marine biologists can use the "fish" model to help classify and identify various fish species in their work, saving time and effort when studying marine ecosystems or conducting biodiversity assessments.
Fishing industry monitoring: Fisheries and government agencies can use the model to monitor fishing activities, identify commonly caught fish species, and ensure that fishing quotas, regulations, and endangered species protections are adhered to. This can help promote sustainable and responsible fishing practices.
Quality control in seafood processing: Seafood processing facilities can utilize the "fish" model to quickly identify and separate different fish species during processing, reducing the risk of cross-contamination, mislabeling, and ensuring consistent product quality for consumers.
Support for recreational fishermen: The computer vision model can be implemented in smartphone applications or dedicated devices, helping recreational fishermen distinguish between various fish species, learn more about their catches, and adhere to regional fishing guidelines and catch limits.

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{
                            fish-rqwia_dataset,
                            title = { fish Dataset },
                            type = { Open Source Dataset },
                            author = { Korea Univ },
                            howpublished = { \url{ https://universe.roboflow.com/korea-univ-mpedr/fish-rqwia } },
                            url = { https://universe.roboflow.com/korea-univ-mpedr/fish-rqwia },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-04-27 },
                            }