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food-detection-dhith/3 (latest)
food detection Computer Vision Project
Angie Tseng
Updated 2 years ago
515
views21
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Classes (30)
baby corn
bean sprout
black glutinous rice
boiled egg
brocoli
cabbage
carrot
chicken breast
chicken leg
corn
cucumber
dark green leaf vegetable
fried chicken
fried egg
fried tofu
green bean
green pepper
oily tofu
okra
pork chop
rice
salmon
sausage
scrambled eggs with tomatoes
shred chicken
shrimp
shrimp roll
stewed pork
sweet potato
tomato
Metrics
mAP is equal to the average of the Average Precision metric across
all
classes in a
model.
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mAP
Precision measures how often your model's predictions are correct.
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Precision
Recall measures what percentage of relevant labels were successfully
identified.
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Recall
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Description
Here are a few use cases for this project:
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Nutrition Tracking Apps: These apps could utilize the "food detection" model to automatically recognize and track the food items consumed by users, providing an easier way to maintain a food diary for health and wellness goals.
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Smart Kitchen Applications: The model could be integrated into smart refrigerators or pantry management systems to recognize and keep track of food items available, indicating when certain items need to be restocked or used before they go bad.
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Food Delivery Services: The model could be used to verify meals against order details for quality checks and ensuring the correct meal is being sent out for delivery, thus reducing errors.
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Dietary Monitoring in Healthcare Facilities: In hospitals or elderly care homes, it can help monitor patients' meals to ensure they are receiving and consuming the right food according to their dietary requirements.
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Interactive Cooking Assistants: Cooking assistance applications could use the model to instruct users on what ingredients to add at each stage of a recipe by recognizing the food items in real time.
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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-detection-dhith_dataset,
title = { food detection Dataset },
type = { Open Source Dataset },
author = { Angie Tseng },
howpublished = { \url{ https://universe.roboflow.com/angie-tseng-onjl3/food-detection-dhith } },
url = { https://universe.roboflow.com/angie-tseng-onjl3/food-detection-dhith },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jun },
note = { visited on 2025-04-01 },
}
