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Vehicles-Object-Detection

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

image

image

📁 Dataset Used :

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. train folders

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

download (14)

download (16)

download (15)

Testing Video Demo

FFVS.mp4