Vehicles-Object-Detection

Overview :To detect and localize vehicles in images or videos is the goal of this project.

📁 Dataset Used :

• https://public.roboflow.ai/object-detection/undefined

The dataset consists of 5 classes:

• Bus
• Truck
• Car
• Traffic signal
• Truck

  Workflow:

  Data Preparation:

  • Create a bounding boxes with the help of label-img And makesense.ai website according to YoloV5.
  • Prepare folder structure that can be accept by YoloV5.

Steps to use Yolov5:

• Cloning the YoloV5 file from official repository.
• Changing the directory of yolov5
• Installing the dependencies
• Download all versions pre-trained weights.

Steps Before Training Custom Dataset:

• Go to yolov5/data/.
• Open data.yaml
• Edit the following inside it:

1. Training and Validation file path
2. Number of classes and Class names.

Training YOLOV5 Model

• Set images size 640 with batch of 8.
• Total 633 images for training and 165 images for validation present in 5 classes.
• Train model around 600 epochs .Stopping training early as no improvement was observed in last 100 epochs. Best results observed at epoch 201, best model saved as best.pt.
• Visualise the training metrics with the help of tensorboard.

Testing Images Using Test Data

Testing Video Demo

FFVS.mp4