Open Images is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
Step1)-- Check for your convinient dataset on https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=detection&c=%2Fm%2F044r5d
Step2)-- Go to--> Type--> Select Detection
Step3)-- Go to--> Options --> Deselect-- Display Boxes from all Catagory
Step4)-- Make Sure to input the same class you Searched after "--classes" in the COMMAND BELOW
-- There is e.g. in COOMAND BELOW for class: "Frying Pan" and "Refrigerator"
Step5)-- Mention the command --limit and the number of images for each class
Step6)-- Modify Below command accordingly and Run in the directory of "OIDV4_Toolkit" where "main.py" is present
Step7)-- Type "Y" If asked for the permission
Run the following command
-- python main.py downloader --classes Frying_Pan Refrigerator --type_csv train --limit 200 --multiclasses 1
Step8)-- After the download your dataset of Yolo wiil be present in OIDv4_Toolkit-Custom-Dataset-Collector-->OID-->Dataset-->train
Step9)-- In the downloaded repository you will get "classes.txt" Modify that with your own classes //ONE CLASS PER LINE
Step10)-- Run python "convert_annotations.py"
Your CUSTOM YOLO DATASET IS READY
"We don't need no bounding-boxes: Training object class detectors using only human verification"Papadopolous et al., CVPR 2016.
Refered from the video on Youtube Reference: https://www.youtube.com/watch?v=_4A9inxGqRM