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damaged-car-parts-prediction-using-yolov8's Introduction

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

Screenshot from 2023-03-18 19-50-29

Results

F1_curve P_curve PR_curve R_curve

Prediction

val_batch0_pred val_batch1_pred val_batch2_pred

Deployment

Screenshot from 2023-03-20 06-11-43

Usage

  • Clone the repo
  • cd to my_fastapi_app
  • run python main.py

Test with images Done!

damaged-car-parts-prediction-using-yolov8's People

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Forkers

cienciadedadosebigdata durgamvaishnavi irwannafly13 sdmwd

damaged-car-parts-prediction-using-yolov8's Issues

# Load the TFLite model and allocate tensors.

Load the TFLite model and allocate tensors.

interpreter = tf.lite.Interpreter(model_path="/home/surya/Desktop/tflite_models/model.tflite")
interpreter.allocate_tensors()

{ model.tflite } n'est pas disponible dans le dossier

Merci de le partager , excellent travaille

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