/gear-teeth-defect-detection-YOLOv3

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gear-teeth-defect-detection-YOLOv3

Download YOLOv3 weights at the very first step

$ wget https://pjreddie.com/media/files/yolov3.weights

Annotation the images with LabelImg

  • To Install LabelImg -
git clone https://github.com/heartexlabs/labelImg.git

conda create -n LabelImg python=3.9.13

conda activate LabelImg
pip install labelimg
  • Open LabelImg
# activate the conda environment
conda activate LabelImg

# cd to the cloned labelImg-Repo
ls
cd lebelImg-Repo
ls

# open labelImg tool
python labelImg.py
  • Annotation procedure;

convert annotated xml to csv_file

# For training-set
python xml_to_csv.py --path_to_xml /path/to/train_images_folder --path_to_csv /path/to/train_images_folder/annotation.csv

# For testing-set
python xml_to_csv.py --path_to_xml /path/to/test_images_folder --path_to_csv /path/to/test_images_folder/annotation.csv

Create labelmap.pbtxt

/img_xml_data/labelmap.pbtxt

Convert csv_file to annotation.txt file in a separate data folder(yolov3_data)

# For train_gear_annotations.txt
python prepare_data.py --path_to_images /path/to/train_images_folder --path_to_csv_annotations /path/to/train_images_folder/annotation.csv --path_to_save_output /yolov3_data/train

# For test_gear_annotation.txt
python prepare_data.py --path_to_images /path/to/test_images_folder --path_to_csv_annotations /path/to/test_images_folder/annotation.csv --path_to_save_output /yolov3_data/test

Adding class names

/classes/gear_teeth.names

Changing the necessary parameters in configuration file

/core/config.py Change the preparameter & hyperparameters for model training based on the machine being trained on;

  • add the class name path ('/classes/gear_teeth.names') / __C.YOLO.CLASSES
  • training annotation path ('/dataset/train_gear_annotations.txt') / __C.TRAIN.ANNOT_PATH
  • training batch size (depending on GPU size) / __C.TRAIN.BATCH_SIZE
  • training input size of neurons (depending on GPU size) / __C.TRAIN.INPUT_SIZE
  • data augmentation (True or False) / __C.TRAIN.DATA_AUG
  • initial and final learning rate / __C.TRAIN.LR_INIT, __C.TRAIN.LR_END
  • numbers of epochs / __C.TRAIN.EPOCHS
  • testing annotation path ('/dataset/test_gear_annotations.txt') / __C.TEST.ANNOT_PATH

Note Model accuracy & performance will be depending on some of the hyperparameters such as epochs, batch size, neuron sizes and learning rate.

Training YOLOv3 Object Detection Model

After the changing and adding some parameter in configuration file.

python train.py

Analyzing the results of training

After the model had been trained, the performance of the model was visualized and analyzed on Tensorboard using the trained log.

tensorboard --logdir './result_output/log'

Evaluating the trained model on the testing set

Test the trained model on the testing set of data, and then check the result of tested image with bounding boxes in ./result_output/eval_detection.

python test.py

Compute mean average precision (mAP) of the trained model

To compute mAP of the trained model;

python mAP/main.py

The computed mAP can be checked in ./results/mAP.png.

The mAP on each class achieved the best result with accurately detected score.

Save the model

The trained weights of the model is saved as TF2 model format for production ready by running

python ckpt_to_savedModel.py

After running, the trained & production-ready model will be save in ./SavedModel/YOLOv3_model/

Inferencing the model on inferencing data (new images)

To inference the model;

python run_inference.py --path_to_images './inference_data/'

Sample images after inferencing

References;

https://github.com/sniper0110/YOLOv3

https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3