Generate train.txt/val.txt/test.txt files under ./data/my_data/ directory. One line for one image, in the format like image_absolute_path box_1 box_2 ... box_n. Box_format: label_index x_min y_min x_max y_max.(The origin of coordinates is at the left top corner.)
NOTE: You should leave a blank line at the end of each txt file.
(2) class_names file:
Generate the data.names file under ./data/my_data/ directory. Each line represents a class name.
For example:
bird
car
bike
...
The COCO dataset class names file is placed at ./data/coco.names.
(3) prior anchor file:
Using the kmeans algorithm to get the prior anchors:
python get_kmeans.py
Then you will get 9 anchors and the average IOU. Save the anchors to a txt file.
The COCO dataset anchors offered by YOLO v3 author is placed at ./data/yolo_anchors.txt, you can use that one too.
NOTE: The yolo anchors should be scaled to the rescaled new image size. Suppose your image size is [W, H], and the image will be rescale to 416*416 as input, for each generated anchor [anchor_w, anchor_h], you should apply the transformation anchor_w = anchor_w / W * 416, anchor_h = anchor_g / H * 416.
4.2 Training
Using train.py. The parameters are as following:
$ python train.py -h
usage: train.py
net_name = 'the yolo model'
anchors_name = 'the anchors name'
body_name = 'the yolo body net'
data_name = 'the training data name'
Check the train.py for more details. You should set the parameters yourself.
Some training tricks in my experiment:
the yolov3 using darknet53, the yolov3_tiny using darknet19
Credits:
I refer to many fantastic repos during the implementation: