Open Carringbrinks opened 1 year ago
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), "box_caption": "%s %.3f" % (names[cls], conf), "scores": {"class_score": conf}, "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] print(box_data) boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
作者您好，这里的for *xyxy, conf, cls in pred.tolist()中的pred.tolist()应该是pred[:, :6].tolist()，您觉得是吗？另外问您一个问题，训练集中的landmark有三个属性，请问第三个可见是1不可见是0吗，加入说做三个点检测，有一个点不可见，landmark第三个属性是不是应该设置为1，1，0，非常期待您的耐心解答
欢迎pr
derronqi
commented
1 year ago derronqi
commented
1 year ago
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), "box_caption": "%s %.3f" % (names[cls], conf), "scores": {"class_score": conf}, "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] print(box_data) boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
作者您好，这里的for *xyxy, conf, cls in pred.tolist()中的pred.tolist()应该是pred[:, :6].tolist()，您觉得是吗？另外问您一个问题，训练集中的landmark有三个属性，请问第三个可见是1不可见是0吗，加入说做三个点检测，有一个点不可见，landmark第三个属性是不是应该设置为1，1，0，非常期待您的耐心解答