Fine-tuning YOLOv9

Step-by-step guide for training and fine-tuning YOLOv9 on custom datasets in Google Colab

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In this guide, we’ll fine-tune YOLOv9 on your custom datasets. You can also use Google Colab to speed up training.

Photo by Alex Shuper on Unsplash

Task

Begin by choosing the appropriate task. YOLOv9 is an object detection model. Start by defining which objects you want to detect. Let’s create the training configuration YAML file:

# train_model.yaml
path: /content/gdrive/MyDrive/path/to/dataset
train: train
val: val
nc: 1
names:
  0: hands

• path, train, val: The path to our training and validation data.
• nc: The number of classes.
• names: The names of all classes.

Dataset

After choosing a task, we’ll select a dataset to work with. I’ll be working with grayscale images. Choose whatever dataset suits your project. The dataset needs to be in YOLO object detection format, meaning each image shall have a corresponding text file:

<class> <center_x> <center_y> <width> <height>
...
<class> <center_x> <center_y> <width> <height>