German-Traffic-Sign-Detction

Detect The German Traffic Sign Using YoloV5

DATA COLLECTION:

Data Collected form Rubixe For Educational Internship

TASK: Object Detection

DATA INFORMATION:

• Total 4290 traffic sign images are present in 39 classes Each class contain 110 images
• Split data with the help of splitfolder library.
• 88 Images for training and 22 images for validation
• Create bounding boxes with the help of label-img tool and makesense.ai website

German Traffic Signs

DATA PREPRATION:

• Prepare folder structure that can be accept by YoloV5.
• Total 3438 images for training and 857 images for validation present in 39 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 128 with batch of 8
• Train model on 50 epochs
• Gives the data file path as well as give pre-trained weights path.

VISUALISE THE TRAINING METRICS WITH THE HELP OF TENSORBOARD

AFTER TRAINING THE MODEL

VALIDATION IMAGES PREDICTION:

PREDICTED IMAGES:

CHALLENGES FACED:

• Facing problem to understand the business case.
• challenge faced in bounding boxes creation
• Assign same no for all classes
• Made mistake in yolov5 folder structure
• Take lots of time to create bounding boxes

WHAT WE LEARN:

• Convert classification task to object detection to improve skill in object detection
• Understand the YoloV5 folder structure as well as learn label-img tool.
• Learn pytorch library.