Damaged-Car-parts-prediction-using-YOLOv8

Training

pip install ultralytics

Collect dataset of damaged cars
Annotate them
in this case there are 8 classes namely : damaged door, damaged window, damaged headlight, damaged mirror, dent, damaged hood, damaged bumper, damaged windshield
convert the annotations into YOLO 1.1 format
run main.py
in this particular case i ran for 5000 epochs

Test with images Done!

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
best_saved_model		best_saved_model
data		data
my_fastapi_app		my_fastapi_app
onnx-tensorflow		onnx-tensorflow
runs/detect		runs/detect
test_images/test		test_images/test
variables		variables
1.jpeg		1.jpeg
README.md		README.md
Screenshot from 2023-03-18 19-50-29.png		Screenshot from 2023-03-18 19-50-29.png
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
best.onnx		best.onnx
best.pt		best.pt
config.yaml		config.yaml
deployment.py		deployment.py
fingerprint.pb		fingerprint.pb
last.onnx		last.onnx
last.pt		last.pt
main.py		main.py
model conversion.ipynb		model conversion.ipynb
predict.py		predict.py
saved_model.pb		saved_model.pb
val.py		val.py
yolov8n.pt		yolov8n.pt