Food Waste Detection

Object Detection

Food Waste Detection Computer Vision Project

Food

Updated a year ago

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Classes (39)
Apple
Apple-core
Apple-peel
Banana Bone
Bone-fish
Bread
Bun
Chicken-skin
Congee
Cucumber Drink
Egg-hard
Egg-scramble
Egg-shell
Egg-steam
Egg-yolk
Fish Meat Mushroom
Mussel
Mussel-shell
Noodle Orange
Orange-peel
Other-waste
Pancake
Pasta Pear
Pear-core
Pear-peel
Potato Rice Shrimp
Shrimp-shell
Tofu
Tomato Vegetable
Vegetable-root

Description

Overview:

Food waste detection dataset.

Labelling instruction:

  1. Label Every Object of Interest in Every Image
  2. Label the Entirety of an Object
  3. Label Occluded Objects, as if they were fully visible (It is a common misconception that boxes cannot overlap.)
  4. Create Tight Bounding Boxes. The edges of bounding boxes should touch the outermost pixels of the object that is being labeled.
  5. Create Specific Label Names (e.g. white pawn, black pawn, green apple, red apple). We can regroup them later into the same class within roboflow preprocessing step .
  6. Maintain Clear Labeling Instructions

Cite This Project

LICENSE
CC BY 4.0

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

                        @misc{
                            food-waste-detection-jghxg_dataset,
                            title = { Food Waste Detection Dataset },
                            type = { Open Source Dataset },
                            author = { Food },
                            howpublished = { \url{ https://universe.roboflow.com/food-1b74y/food-waste-detection-jghxg } },
                            url = { https://universe.roboflow.com/food-1b74y/food-waste-detection-jghxg },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jul },
                            note = { visited on 2025-07-10 },
                            }
                        
                    

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