𝐓𝐑𝐀𝐅𝐅𝐈𝐂 𝐒𝐈𝐆𝐍 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 𝐘𝐎𝐋𝐎𝐕5

Detect The Traffic Sign Using YoloV5

𝐃𝐀𝐓𝐀 𝐂𝐎𝐋𝐋𝐄𝐂𝐓𝐈𝐎𝐍:

Data Collected from Roboflow website.

𝐓𝐀𝐒𝐊: 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧

𝐃𝐀𝐓𝐀 𝐈𝐍𝐅𝐎𝐑𝐌𝐀𝐓𝐈𝐎𝐍:

• Total 3708 traffic sign images are present in 43 classes .
• Total 2538 images for training and 701 images for validation present in 2 classes.
• Create bounding boxes with the help of makesense.ai website.

  𝐓𝐑𝐀𝐅𝐅𝐈𝐂 𝐒𝐈𝐆𝐍

𝐃𝐀𝐓𝐀 𝐏𝐑𝐄𝐏𝐑𝐀𝐓𝐈𝐎𝐍:

• Prepare folder structure that can be accept by YoloV5.

• Total 3708 images for training and 352 images for validation present in 43 classes.

• Create a bounding boxes with the help of label-img And makesense.ai website according to 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:

1. Go to yolov5/data/

2. Open coco128.yaml

3. Edit the following inside it:

   A. Training and Validation file path

   B. Number of classes and Class names.

𝐓𝐑𝐀𝐈𝐍𝐈𝐍𝐆 𝐘𝐎𝐋𝐎𝐕5 𝐌𝐎𝐃𝐄𝐋:

• Set images size 408 with batch of 8
• Train model on 1000 epochs
• Gives the data file path as well as give pre-trained weights path.

VISUALISE THE TRAINING METRICS WITH THE HELP OF TENSORBOARD

𝐏𝐑𝐄𝐃𝐈𝐂𝐓𝐄𝐃 𝐈𝐌𝐀𝐆𝐄𝐒:

𝐓𝐄𝐒𝐓 𝐕𝐈𝐃𝐄𝐎

vid.traffci.3.mp4