What is the annotation format of yolov6_seg algorithm ([cls_id, x1, y1, x2, y2, ...] or [cls_id, x_c, y_c, box_w, box_h, x1, y1, x2, y2, ...]])

[zjykzj]

Before Asking

• I have read the README carefully. 我已经仔细阅读了README上的操作指引。

• I want to train my custom dataset, and I have read the tutorials for training your custom data carefully and organize my dataset correctly; (FYI: We recommand you to apply the config files of xx_finetune.py.) 我想训练自定义数据集，我已经仔细阅读了训练自定义数据的教程，以及按照正确的目录结构存放数据集。（FYI: 我们推荐使用xx_finetune.py等配置文件训练自定义数据集。）

• I have pulled the latest code of main branch to run again and the problem still existed. 我已经拉取了主分支上最新的代码，重新运行之后，问题仍不能解决。

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• I have searched the YOLOv6 issues and found no similar questions.

Question

I tried to train the segmentation algorithm for the YOLOv6_seg branch (https://github.com/meituan/YOLOv6/tree/yolov6-seg) and found that it uses YOLO format to segment annotated data, which is different from the Ultralytics project

The segmentation data annotation format for Ultralytics Engineering is

[cls_id, x1, y1, x2, y2, ...]

I observed the YOLOv6 project code and found that the format of the loaded annotation file is (yolov6/data/seg_dataset.py check_label_files / get_segment)

[cls_id, x_c, y_c, box_w, box_h, x1, y1, x2, y2, ...]

I tried to search the document, but it only provided the annotation format for the detection algorithm：https://github.com/meituan/YOLOv6/blob/yolov6-seg/docs/Train_custom_data.md

Additional

No response

[zjykzj]

@Chilicyy @benicamera @meituan-gengyifei @mtjhl @SangbumChoi An obvious logical error in

YOLOv6/yolov6/data/seg_datasets.py

Line 288 in 4171e41

def get_segment(labels):

    @staticmethod
    def get_segment(labels):
        rlabels = []
        segments = []
        if len(labels) == 0:
            return np.asarray([])
        for label in labels:
            z1 = []#labels
            z2 = []#seg
            for l in label:
                z1.append(np.asarray(l[:5]).reshape(1, 5).astype(np.float32))                     <------------- this
                z2.append(np.asarray(l[1:]).reshape(-1, 2).astype(np.float32))                    <------------- this
            if z1:
                rlabels.append(np.concatenate(z1, axis = 0))
                segments.append(z2)
            else:
                t = np.zeros((1, 5), dtype = np.float32)
                t[..., 0]= -1
                rlabels.append(t)
                segments.append([np.zeros((2, 2), dtype = np.float32)])
        return rlabels, segments