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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

About

The goal of this project is to detect and localize vehicles in images or videos.

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