TRY THIS MODEL
Drop image here to test
ng-object-detection/3 (latest)
NG Object Detection Computer Vision Project
Peter Nguyen
Updated 4 months ago
Tags
Classes (86)
BBQ sauce
Bamboo
Barrel
Basket
Blue Chair
Book trolley
Bookrow
Bread
Brown Chair
Buoy
Calamari Rings
Cherry
Chichdi
Chicken Wings
Chutney
Cofee
Coffee Cup
Cucumber
Cupcake
Curry Sauce
Dark Green Chair
Dolma
Eating Mat
Glass
Grass
Green Chair
Jet Ski
Kesar Bath
Ketchup
Kichdi
Kofta
Leaf
Lettuce
Light
Mashed Potato
Mashed potatoes
Meatballs
Mountain
Naan
Orange Chair
Pillow
Plant
Plastic chair
Plate
Plum Chair
Queso
Questions?
Ranch
Rasam
Rasberry
Red Chair
Red seat
Rice
Sand
Sea
Shelf
Sign
Smores
Table
Tomato
Upma
Utensils
Vada
Vase
Water bottle
White Chair
Wooden Chair
Yellow Chair
book
carrots
chair
chickem
coaster
commands
cushion
hand
holder
ice cream
lime
objects
ocean
onions
patty
person
seats
sidebar
Metrics
mAP is equal to the average of the Average Precision metric across
all
classes in a
model.
Learn more
mAP
Precision measures how often your model's predictions are correct.
Learn more
Precision
Recall measures what percentage of relevant labels were successfully
identified.
Learn more
Recall
Try This Model
Drop an image or
A description for this project has not been published yet.
Use This Trained Model
Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.
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{
ng-object-detection_dataset,
title = { NG Object Detection Dataset },
type = { Open Source Dataset },
author = { Peter Nguyen },
howpublished = { \url{ https://universe.roboflow.com/peter-nguyen/ng-object-detection } },
url = { https://universe.roboflow.com/peter-nguyen/ng-object-detection },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { oct },
note = { visited on 2025-03-05 },
}
