YOLOv3 with quadrangle

Reimplementation of YOLOv3 with quadrangle

This is a reimplementation of YOLOv3: An Incremental Improvement and is based on Ultralytics LLC's PyTorch implementation. This work detects obejcts in arbitrary directions with quadrangle, and implemented on ICDAR2015 text dataset for example.

Requirements

• Python3
• numpy
• torch
• opencv-python
• Shapely

Train

Check the -data_config_path and -cfg in train.py.

Dataset folder is organized as follows:

• Dataset
  • images
  • labels

The label format: class x1 y1 x2 y2 x3 y3 x4 y4

For example (0 493 115 519 115 519 131 493 131)

$ python3 train.py

Train for your own dataset

• Modify yolov3.cfg file
  • Change [yolo] classes with the number of classes in your own dataset.
  • Replace the value of filters in [convolutional] which lays above [yolo], filters should be 3 * (8 + 1 + num_classes), where 8 means 8 offsets of the quadrangle, 1 means objectness confidence.
• Modify cfg/*.data where classes field should be your number of classes in your dataset
• Modify data/*names and put your labels in it.

Inference

Checkpoints are saved in weights.

$ python3 detect.py