vegetable_detection Semantic Segmentation Dataset and Pre-Trained Model by spectacle

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Explore Dataset

Here are a few use cases for this project:

Grocery Sorting Automation: This computer vision model can be utilized in grocery stores or supermarkets for automatic sorting and labelling of different types of vegetables, enhancing efficiency and reducing human errors.
Smart Agriculture: Farmers can use this model to identify and separate harvested vegetables, streamlining their farming processes.
Dietary Apps: This model can be integrated into diet tracking or meal planning apps, helping users to recognize and log the vegetables they consume and better manage their nutrition.
Educational Tools: In educational contexts, this model can be used to create interactive learning tools for students studying botany, nutrition, or cooking, helping them to differentiate various types of vegetables.
Culinary Applications: Restaurants or culinary schools could use this model in an app or system to assist in identifying different vegetables needed for recipes, ensuring accurate preparation and cooking.

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{
                            vegetable_detection_dataset,
                            title = { vegetable_detection  Dataset },
                            type = { Open Source Dataset },
                            author = { spectacle },
                            howpublished = { \url{ https://universe.roboflow.com/spectacle/vegetable_detection } },
                            url = { https://universe.roboflow.com/spectacle/vegetable_detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-07-27 },
                            }