Food Ingredients Image Detection_Team4 Computer Vision Project

DSStudy

Updated a year ago

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Classes (166)
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10_pineapple
10_tangerine
Apple
Bagel
Banana
Bitter melon
Cucumber Garlic
Green Chili
Lady finger
Mushroom Onion Orange Potato
Sponge Gourd
Tomato
ampalaya
apple
asparagus
avocado
bacon
banana beef bell pepper bell_pepper
bento
bitter gourd
blueberries
bok choy
bottle bread broccoli butter cabbage can
canned_tuna
carrot
cashew
cauliflower cheese cherry chicken
chicken breast
chicken thigh
chicken wing
chicken_breast
chilli
chocolate corn crab
cream-cheese
cucumber dates egg egg_ eggplant eggs fish
fish_cake
flour
french_fries
garlic ginger
goat_cheese
grapefruit
grapes
grated_cheese
green chilli
green-chillies
green_beans
green_onion
ground_beef
guacamole
ham
hash_brown
heavy_cream
humus
juice
ketchup
kimchi
kiwi
leek
lemon lettuce lime
lobster tails
mango
marmelade
mayonaise
mayonnaise
milk
mint
mozzarella cheese
mushrooms
mustard
nacho_chips
nuts
olives
onion orange
oysters
pak_choi
parmasan_cheese
parsley
pasta
pawpaw
peach
peanuts
pear peas pepper
pickles
pimento
pineapple
plantains
plasticsaveholder
pomegrante pork
pork belly
pot
potato
potatoes
pudding
pumpkin
radish
red chili
red_cabbage
red_grapes
red_onion
rice
rice_ball
rice_cake
salad
salami
salmon sandwich sausage
sayote
sea scallops
seaweed
seseme
shrimp
smoothie
spinach
spring_onion
strawberries
strawberry sugar sweet potato sweet_potato
tangerine
tempeh
tofu
tomato
tomato_sauce
tortillas
tuna
turkey
watermelon
white rice
yogurt zucchini
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Description

Here are a few use cases for this project:

  1. Meal Preparation: Users can leverages the "Food Ingredients Image Detection_Team4" model to facilitate their meal preparations, helping them identify specific ingredients on their kitchen counters or in their pantry. This can even be employed in cooking apps or chef assistants to verify if a user has the correct ingredients needed for a certain recipe.

  2. Grocery Shopping: As part of a grocery shopping app, the AI model can help shoppers identify different ingredients in the store. In addition, it can assist users in finding replacements for items that aren't available or suggest alternatives based on dietary restrictions.

  3. Dietary Tracking: Nutrition apps can use this model to help users identify and track the ingredients they're consuming. By scanning food items or meals, users can acquire a detailed understanding of their dietary habits, which can support weight loss goals or managing dietary restrictions.

  4. School Cafeteria: Schools can implement this model to help in their cafeterias, identifying specific ingredients in meals to accommodate students with food allergies or dietary preferences.

  5. Food Waste Management: By identifying perishable items like fruits and vegetables, this model can support food waste reduction strategies at home or in food-related businesses. It would alert users to consume items that are nearing their expiration date.

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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-ingredients-image-detection_team4_dataset,
                            title = { Food Ingredients Image Detection_Team4 Dataset },
                            type = { Open Source Dataset },
                            author = { DSStudy },
                            howpublished = { \url{ https://universe.roboflow.com/dsstudy-h0rzy/food-ingredients-image-detection_team4 } },
                            url = { https://universe.roboflow.com/dsstudy-h0rzy/food-ingredients-image-detection_team4 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-11-05 },
                            }
                        
                    

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