vedai-Yolov8

A python script to train a YOLO model on Vedai dataset and Detection script that detects the bounding box and use SORT algorithm for tracking.

Requirements

pip install -r requirements.txt

Train

1. Prepare training data

1.1 Download VEDAI dataset for our training from VEDAI

1.2 Unzip the dataset and arrange the files in provided order for transforming script to work :

├── dataset
│   ├── VEDAI
│   │   ├── images
│   │   ├── labels
│   │   ├── fold01.txt
│   │   ├── fold01test.txt
│   │   ├── fold02.txt
│   │   ├── .....
│   ├── VEDAI_1024
│   │   ├── images
│   │   ├── labels

1.3 Run the transform.py script to convert the annotation format from PascalVOC to YOLO Horizontal Boxes.

1.4 Classify the images in train, val and test with the following folder structure :

├── data
│   ├── train
│   │   ├── images
│   │   ├── labels
│   ├── val
│   │   ├── images
│   │   ├── labels
│   ├── test
│   │   ├── images
│   │   ├── labels

Note : Adjust the path='dataset' before running the script.

2. Begin the training using the CLI command :

2.1 Update data.yaml with the location of dataset 2.2 Run the following CLI command

yolo task=detect mode=train epochs=100 data=data.yaml model=yolov8m.pt imgsz=512 batch=8

Test

1. The trained weight would be stored in runs/detect/train/weights/
2. Run Detection.py with the updated location of the weight

Note : Update the location for video file

Detection results

Sample Run

Pretrained vs Vedai