[How to use]

1. you need dataset.

• dataset structure is..
  

  /train/0.jpg, /train/0.txt, /valid/0.jpg, /valid/0.txt, ....

• 0.txt contain position and label of objects like below
  

  (xmin, ymin, xmax, ymax, label)

  1273.0 935.0 1407.0 1017.0 v1

  911.0 893.0 979.0 953.0 v1

  984.0 889.0 1053.0 948.0 v1

• To encode label name to integer number, you should define labels in the 'class_lable_map.xlsx"

  v1 1

  v2 2

  ....

  * start from 1. not from 0. 0 will be background (in the loss.py).

2. need some settings for dataset reader.

   - see train.py. you can find some code for reading dataset

   
     'trainset = ListDataset(root="../train", gt_extension=".txt", labelmap_path="class_label_map.xlsx", is_train=True, transform=transform, input_image_size=512, num_crops=n_crops, original_img_size=2048)'
     

   • you should set the 'input_image_size' and 'original_img_size'. 'input_image_size' is size of (cropped) image for train. And 'original_img_size' is size of (original) image. I made this parameter to handle high resolution image. if you don't need crop function, -1 for num_crops.

3. Train with your dataset!

   you should define some parameter like learning rate, which optimizer to use, size of batch etc.