Using the training images located in TrainYourOwnYOLO/Data/Source_Images/Training_Images and the annotation file data_train.txt which we have created in the previous step we are now ready to train our YOLOv3 detector.
Before getting started download the pre-trained YOLOv3 weights and convert them to the keras format. To run both steps run the download and conversion script from within the TrainYourOwnYOLO/2_Training directory:
python Download_and_Convert_YOLO_weights.py
To list available command line options run python Download_and_Convert_YOLO_weights.py -h.
The weights are pre-trained on the ImageNet 1000 dataset and thus work well for object detection tasks that are very similar to the types of images and objects in the ImageNet 1000 dataset.
To start the training, run the training script from within the TrainYourOwnYOLO/2_Training directory:
python Train_YOLO.py
Depending on your set-up, this process can take a few minutes to a few hours. The final weights are saved in TrainYourOwnYOLO/Data/Model_weights. To list available command line options run python Train_YOLO.py -h.
If training is too slow on your local machine, consider using cloud computing services such as AWS to speed things up. To learn more about training on AWS navigate to TrainYourOwnYOLO/2_Training/AWS.
Next, go to TrainYourOwnYOLO/3_Inference to test your YOLO detector on new images!