YOLOv3 (Detection, Training, and Evaluation)

Used convert.py of https://github.com/david8862/keras-YOLOv3-model-set to export the base YOLOv3 Keras Model.

• YOLOv3 Weights file https://pjreddie.com/media/files/yolov3.weights

• Config (cfg) File https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg

Installing

To install the dependencies, run

pip install -r requirements.txt

And for the GPU to work, make sure you've got the drivers installed beforehand (CUDA).

It has been tested to work with Python 2.7.13 and 3.5.3.

Training

1. Data preparation

Organize the dataset into 4 folders:

• train_image_folder <= the folder that contains the train images.

• train_annot_folder <= the folder that contains the train annotations in VOC format.

• valid_image_folder <= the folder that contains the validation images.

• valid_annot_folder <= the folder that contains the validation annotations in VOC format.

There is a one-to-one correspondence by file name between images and annotations. If the validation set is empty, the training set will be automatically splitted into the training set and validation set using the ratio of 0.8.

2. Edit the configuration file

3. Start the training process

python train.py -c config.json

By the end of this process, the code will write the weights of the best model to file best_weights.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file).

4. Perform detection using trained weights on image, set of images, video, or webcam

python detect.py -c config.json -i /path/to/image/or/video

Config file must contain anchors, traffic light labels and model, traffic sign labels and model.

It carries out detection on the image and write the image with detected bounding boxes to the same folder.

If you wish to change the object threshold or IOU threshold, you can do it by altering obj_thresh and nms_thresh variables. By default, they are set to 0.5 and 0.45 respectively.

Evaluation

python evaluate.py -c config.json

Compute the mAP performance of the model defined in saved_weights_name on the validation dataset defined in valid_image_folder and valid_annot_folder.