Dataset
The dataset was referenced in the Smartathon competition.It's consist of 7874 images annontated with 11 classes:
- GARBAGE 8597
- CONSTRUCTION_ROAD 2730
- POTHOLES 2625
- CLUTTER_SIDEWALK 2253
- BAD_BILLBOARD 1555
- GRAFFITI 1124
- SAND_ON_ROAD 748
- UNKEPT_FACADE 127
- FADED_SIGNAGE 107
- BROKEN_SIGNAGE 83
- BAD_STREETLIGHT 1
The dataset highly imbalanced and contain some humman errors.
Our SEE Team Solution
- Convert from Pascal VOC to YOLO format
- Model Hyperparamter tuning
- Train the data on Yolov7
- Evaluate the model
- Expalin Different techniques to Automation of Data Annotation
For our solution detials: notebook
How to use
- You can just download file weights from the files section
- clone yolov7 repo
!git clone https://github.com/WongKinYiu/yolov7 - ensure your current working directory is yolov7 then run
! pip install -r requirements.txt - then run the detector script
! python detect.py --weights " model.pt path" --img 736 --conf 0.27 --source "testing image path" --save-txt