food Computer Vision Project

yolostudy

Updated 2 years ago

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Classes (66)
Fish_cake
Kimbab_Ham
Lotus_root
Perilla_leaf
White_Mushroom
anchovy
asparagus
avocado
bacon
bacon_sprouts
bean
bean_sprouts
beef beet
bok_choy
broccoli
burdock
calamari
canned_saury
canned_tuna
carrot cheese chicken
chives
cockle
corn crab cucumber
curcuma
duck_meat
dumpling
egg egg_plant
enoki_mushroom
fan_mussel
ginseng
green_onion
hairtail
kimchi
lamb_ribs
mackerel
meat_for_soup
milk
napa_cabbage
nurungji
onion
oyster
oyster_mushroom
paprika
pasta_noodles
pickle
pork potato
radish
raw_ribs
rice_cake
salmon sausage
scallop
seaweed
spam
squash
sweet_potato tomato
tomato_sauce
water_parsley

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Description

Here are a few use cases for this project:

  1. Nutrient Content Analyzer: The model could be used in a mobile app that analyzes the nutrient content of a meal by identifying distinct food items, which could benefit diet-conscious individuals or those with specific dietary needs.

  2. Cooking Assistant Application: An application could use this model to assist users in identifying and categorizing ingredients for a recipe. The user would take a photo of their ingredients, and the app would recognize and list them, making recipe preparation more convenient and streamlined.

  3. Inventory Management: Grocery stores or restaurants could utilize the model to automatically track and manage inventory by recognizing specific food items.

  4. Food Waste Management: This model could be useful for creating a system that tracks the rate at which different types of food are thrown away in households, restaurants, or grocery stores, helping to gain insights into food waste patterns.

  5. Allergen Alert System: Those with food allergies could use an app that uses this model to identify potential allergens in prepared meals by recognizing specific food items. The app could alert the user if their allergen is detected, providing an additional safety layer.

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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-k3oqt_dataset,
                            title = { food Dataset },
                            type = { Open Source Dataset },
                            author = { yolostudy },
                            howpublished = { \url{ https://universe.roboflow.com/yolostudy-lxjtn/food-k3oqt } },
                            url = { https://universe.roboflow.com/yolostudy-lxjtn/food-k3oqt },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-11-17 },
                            }
                        
                    

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