kmanjari / yolov3_tiny_tensorflow

Add YOLOv3_tiny and data augment(clip, brighten, change saturation)

YOLOv3 and YOLOv3_tiny for TensorFlow

1. Introduction

Add YOLOv3_tiny and data augment(clip, brighten, change saturation)

2. Requirements

• tensorflow >= 1.8.0 (lower versions may work too)
• opencv-python

3. Running demos

(1) Single image test demo using ckpt file:

python test_single_image.py ./data/demo_data/car.jpg

(2) Single image test demo using pb file:

python test_single_image_pb.py ./data/demo_data/car.jpg

4. Training

4.1 Data preparation

(1) annotation file

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.)

For example:

xxx/xxx/1.jpg 0 453 369 473 391 1 588 245 608 268
xxx/xxx/2.jpg 1 466 403 485 422 2 793 300 809 320
...

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:

https://github.com/wizyoung/YOLOv3_TensorFlow