def main(): "register data" register_dataset() "checkout raw dataset annotation" # checkout_dataset_annotation() "train" trainer=train(train_flag=True,resume_load=True) # single_predict() "check pre annotation" # checkout_pre_annotation(visual_num=10) "predict and merge_nms" # # single_predict() # for i in ["model_0004093.pth"]: # predict_my(visual=True) # # "merge" # merge() # "-------------" "checkout merge predict result annotation " # checkout_pre_annotation() # checkout_dataset_annotation((save_path=os.path.join(cfg.OUTPUT_DIR,"my_merge_visual"),subdata_name="pv_raw_test") "data analysis ,coco format save,output visual,megrgeresult visual--" # result_analysis() """Evaluate object proposal, instance detection/segmentation, keypoint detectionoutputs using COCO's metrics and APIs."""
def main(): "register data" register_dataset() "checkout raw dataset annotation" # checkout_dataset_annotation() "train" train(train_flag=False,resume_load=True) # single_predict() "check pre annotation" # checkout_pre_annotation(visual_num=10) "predict and merge_nms" single_predict()
def main(): "register data" register_dataset() "checkout raw dataset annotation" # checkout_dataset_annotation() "train" # trainer=train(train_flag=False,resume_load=False) # single_predict() "check pre annotation" # checkout_pre_annotation(visual_num=10) "predict and merge_nms" # single_predict() # checkout_pre_annotation(visual_num=5) for i in ["model_0032717.pth"]: predict_my(model_weight=i,visual=True)