Tilapia model Object Detection Dataset and Pre-Trained Model by OMUTilapia

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Explore Dataset

Here are a few use cases for this project:

Aquaculture Management: Tilapia breeders and fish farmers can use the Tilapia model to monitor and track the different Tilapia classes in their fish farms, allowing for better management of breeding programs and overall fish stock health.
Fish Market Quality Control: The model can be utilized at fish markets to ensure only the desired Tilapia classes are selected and sold, improving the quality of fish products and meeting customer preferences.
Biodiversity Research: Researchers and ecologists can use the Tilapia model to study the distribution, population dynamics, and ecological roles of various Tilapia classes in different aquatic ecosystems, supporting conservation efforts and the sustainable management of fisheries.
Fish Identification for Enthusiasts: Aquarium hobbyists and fish enthusiasts can use the model to identify, classify, and determine the compatibility of specific Tilapia classes with their existing fish collections, optimizing the aquarium environment and fish health.
Educational Tools: Educators and researchers can incorporate the Tilapia model into interactive educational materials, such as mobile applications or online platforms, to teach students and the general public about Tilapia species classification, biology, and environmental impacts.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            tilapia-model_dataset,
                            title = { Tilapia model Dataset },
                            type = { Open Source Dataset },
                            author = { OMUTilapia },
                            howpublished = { \url{ https://universe.roboflow.com/omutilapia/tilapia-model } },
                            url = { https://universe.roboflow.com/omutilapia/tilapia-model },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-06-18 },
                            }