Fine-tune YOLOv8 model easily and for free
A simple way to fine-tune your model of YOLOv8 for free.
Dataset
Note : I am sorry for the potential mistakes in my English I am not very good at English
Firstly you must choose a subject. For the tutorial I chose to create a dataset on player detection in Minecraft (a video game) you can take it. But you can download a dataset already created.
So, take photo for your dataset (playing image for mi). We going to use roboflow. Create an account or sign in and create a new project.
Import all the images you need.
Click “save and continue”, wait and get to “annotate/Manual Labeling/assign images”, and finally “start annotating”.
Frame all your objects and do it for all the images.
shortcuts :
— “n” for selecting mode
— “arrow” for changing image
When annotating all the images, you can quit and add the images to the dataset.
(My arrows have the same quality as my english)
Now it’s almost finished you must just go to the “generate” tab.
Push the button “continue”*2 and “create”. Wait a little … and it’s good. You just have to export the dataset in YOLOv9 format.
Training
For the training you just need a google account and a ultralytics account.
Firstly you must go to the ultralytcs page and create a new dataset.
Now create a new model and attach to it the dataset, copy the code in the «google colab» tab. Go to google colab create a new notebook, choose T4 gpu, paste the code and execute it. The model is training !
You can see the loss evolution on the ultralytics hub. And after the train process you can download the model and use it in code (futur acticle).
You can see some example of utilisation.
I hope you have been interested by this acticle and if this is the case you can clap it. =)
