Requirements

• Python>=3.7.0
  
• PyTorch>=1.7
  
• pip install -r requirements.txt

Dataset

The dataset is available on download here

Train

python train.py --img 640 --cfg yolov5s.yaml --hyp hyp.scratch.yaml --batch 32 --epochs 100 --data road_sign_data.yaml --weights yolov5s.pt --name yolo_road_det

Parameters:

• img : Size of image
  
• batch : The batch size
  
• epochs : Number of epochs to train for
  
• data : Data YAML file that contains information about the dataset (path of images, labels)
  
• workers : Number of CPU workers
  
• cfg : Model architecture. There are 4 choices available: yolo5s.yaml, yolov5m.yaml, yolov5l.yaml, yolov5x.yaml
  
• weights: Pretrained weights you want to start training from. If you want to train from scratch, use --weights ' '
  
• name: Various things about training such as train logs. Training weights would be stored in a folder named runs/train/name
  
• hyp: YAML file that describes hyperparameter choices. For examples of how to define hyperparameters, see data/data_uno_cards.yaml

Test

python val.py --data data.yaml --weights model.pt

Parameters:

• data : Data YAML file that contains information about the dataset (path of images, labels)
  
• weights: Pretrained weights to make predictions

Inference

python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images

Note: results will be saved to runs/detect

Parameters:

• weights: Pretrained weights to make predictions
• img : Size of image
  
• conf: Thresholding objectness confidence
  
• source: The source of detector, which can be: single image, folder of images, Video, Webcam

Web application

python run.py --path path_to_model.pt

• path: path to trained weights

Google Colab

• Training_yolov5_Colab.ipynb : train, test
  
• Flask_Web_App.ipynb : flask application
  
• Real_time_object_detection_w_webcam.ipynb : real time object detection

Results

Refer to youtube.

Test data

Labels

Predictions