Image data for training YOLO in Google Colab. The Colab notebook can be found here
Created to properly name images of sheep and coke from collected data in https://github.com/evan-tan/ece4078-team2-05 M3 folder.
Place selected labelled data into sheep/
and coke/
folders respectively.
Then run python data_prep.py
Apart from the labelled data bins and cfg/
, this folder structure is used in https://github.com/AlexeyAB/Yolo_mark. The only difference is that the original img/
folder has been renamed to obj/
as instructed when shifting images for training in https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects Step 4
Run the bash script local_setup.sh
, please ensure that you have the darknet repository in the parent folder where you clone this repository!
- Add enable
data_prep.py
for validation and test sets