发现 cfg/default.yaml 参数 mask_ratio 等于4 直接训练如下边缘分割标签,推理时mask 稀疏，训练时分数偏低,mask_ratio 改为1训练时打印的mask 的 P指标一直为0,将imgsz=原图size 训练分数也不高

标注用的是labelme多边形

阅读源码发现可能是因为mask缩放导致

且出现上边缘mask被box过度剪裁的情况

修改了源码中的两处,还是保持mask_ratio等于4,重新训练,推理如下,虽然mask粗糙但几乎不产生断裂

修改如下:

1.ultralytics/data/utils.py

从

def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    mask = np.zeros(imgsz, dtype=np.uint8)
    polygons = np.asarray(polygons, dtype=np.int32)
    polygons = polygons.reshape((polygons.shape[0], -1, 2))
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
    # Note: fillPoly first then resize is trying to keep the same loss calculation method when mask-ratio=1
    return cv2.resize(mask, (nw, nh))

到

def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    mask = np.zeros((imgsz[0]// downsample_ratio,imgsz[1]// downsample_ratio), dtype=np.uint8)
    polygons=[[j*0.25 for j in i] for i in polygons]
    polygons = np.asarray(polygons, dtype=np.int32)
    polygons = polygons.reshape((polygons.shape[0], -1, 2))
    cv2.fillPoly(mask, polygons, color=color)
    return mask

2.ultralytics/models/yolo/segment/predict.py

类SegmentationPredictor postprocess方法 外扩mask 1个像素

def postprocess(self, preds, img, orig_imgs):
    """Applies non-max suppression and processes detections for each image in an input batch."""
    p = ops.non_max_suppression(
        preds[0],
        self.args.conf,
        self.args.iou,
        agnostic=self.args.agnostic_nms,
        max_det=self.args.max_det,
        nc=len(self.model.names),
        classes=self.args.classes,
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
    for i, pred in enumerate(p):
        orig_img = orig_imgs[i]
        img_path = self.batch[0][i]
        if not len(pred):  # save empty boxes
            masks = None
        elif self.args.retina_masks:
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
            masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
        else:
            #外扩盒子
            c, mh, mw = proto[i].shape  # CHW
            ih, iw = img.shape[2:]
            pred[:, :4][:, 0] -= iw / mw*1
            pred[:, :4][:, 1] -= ih / mh*1
            pred[:, :4][:, 2] += iw / mw*1
            pred[:, :4][:, 3] += ih / mh*1

            masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
    return results