After using HyperLabel (on Windows) to label the images, export it in YOLO format to a directory. Inside that directory there is a "data" directory and inside that will be an "img" directory that contains the images and the labels. Copy this "img" directory to yolov5/training/
Inside training there is a script that will randomly separate the labelled data into train and test sets. Running this will copy images and labels from the "img" directory and place them inside of the yolov5/training/data/ training directory in the respective subdirectories.
Create the dataset.yaml according to the instructions, changing the classes to 1 and the name of the class to "Lizard"
(or whatever you used in HyperLabel).
Copy the model over as per instructions and edit the number of categories to 1. (see instructions)
Run the training as follows from the yolov5 directory:-
Code: Select all
python train.py --img 640 --batch 16 --epochs 300 --data training/dataset.yaml --cfg training/yolov5l.yaml --weights ''
Note that this is different from the instructions which contains an error in spacing (300--data) and has a batch size that is too large for the GPU. This has been reduced to batch size of 2 in order for it to run in some cases. It seems that if that happens, you need to restart the machine to allow a batch size of 16.