how to train yolo on custom data in python

To train YOLO (You Only Look Once) on custom data, follow these steps:

1. Collect and label the custom dataset: The first step is to collect and label the dataset which includes the objects you want YOLO to detect. Then, using labeling tools such as LabelImg, label the objects in your dataset with bounding boxes.

2. Prepare the dataset in YOLO format: To train YOLO on your custom dataset, you need to convert the dataset to the YOLO format. Use tools such as yolov5pytorch/scripts/ultralytics_yolov5/convert.py to convert the dataset to YOLO format.

3. Configure the YOLO model: YOLO has several pre-trained models such as YOLOv3, YOLOv4. Choose the pre-trained YOLO model version and then configure the model by setting parameters such as batch size, image size, number of epochs, and learning rate.

4. Train the YOLO model: Once the data is prepared and the model is configured, train the YOLO model using the custom dataset. This can be done by running the training script provided in the yolov5 repository.

5. Evaluate the YOLO model: Once the model is trained, evaluate its performance on the test dataset by computing the precision-recall curve and the average precision (AP).

Here's an example code to train YOLO on custom data in Python:

main.py
!pip install wandb
!git clone https://github.com/ultralytics/yolov5.git
%cd yolov5

# train YOLO on custom dataset
!python train.py --img 416 --batch 16 --epochs 100 --data custom.yaml --weights yolov5s.pt
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