Yolov8如何在训练意外中断后接续训练

1.错误尝试

在训练YOLOv8的时候，因为开太多其他程序，导致在100多次的时候崩溃，查询网上相关知识如何接着训练，在yolo5中把resume改成True就可以。
在yolov8中也这样尝试，将ultralytics/yolo/cfg/default.yaml中的resume改成True发现并没有作用，感觉yolov8代码还是有很多bug

2.成功的方法

2.1 ultralytics/yolo/engine/model.py

打开ultralytics/yolo/engine/model.py代码，找到train方法，如下
将self.trainer.model = self.model注释掉

    def train(self, **kwargs):        """        Trains the model on a given dataset.        Args:            **kwargs (Any): Any number of arguments representing the training configuration.        """        overrides = self.overrides.copy()        overrides.update(kwargs)        if kwargs.get("cfg"):            LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")            overrides = yaml_load(check_yaml(kwargs["cfg"]), append_filename=False)        overrides["task"] = self.task        overrides["mode"] = "train"        if not overrides.get("data"):            raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")        if overrides.get("resume"):            overrides["resume"] = self.ckpt_path        self.trainer = self.TrainerClass(overrides=overrides)        # if not overrides.get("resume"):  # manually set model only if not resuming        #     self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)        #     self.model = self.trainer.model        #下面一行代码在正常情况下需要开启        # self.trainer.model = self.model        self.trainer.train()        # update model and cfg after training        if RANK in {0, -1}:            self.model, _ = attempt_load_one_weight(str(self.trainer.best))            self.overrides = self.model.args

2.2 ultralytics/yolo/engine/trainer.py

找到check_resume和resume_training方法
在check_resume方法里面将resume=中断地方的last.pt
在resume_training里面将ckpt=中断地方的last.pt

def check_resume(self):        # resume = self.args.resume        resume = 'runs/detect/train49/weights/last.pt';        if resume:            try:                last = Path(                    check_file(resume) if isinstance(resume, (str,      Path)) and Path(resume).exists() else get_latest_run())                self.args = get_cfg(attempt_load_weights(last).args)                self.args.model, resume = str(last), True  # reinstate            except Exception as e:                raise FileNotFoundError("Resume checkpoint not found. Please pass a valid checkpoint to resume from, "            "i.e. 'yolo train resume model=path/to/last.pt'") from e        self.resume = resume    def resume_training(self, ckpt):        ckpt = torch.load('runs/detect/train49/weights/last.pt')        if ckpt is None:            return        best_fitness = 0.0        start_epoch = ckpt['epoch'] + 1        if ckpt['optimizer'] is not None:            self.optimizer.load_state_dict(ckpt['optimizer'])  # optimizer            best_fitness = ckpt['best_fitness']        if self.ema and ckpt.get('ema'):            self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA            self.ema.updates = ckpt['updates']        if self.resume:            assert start_epoch > 0, \                f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \                f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"            LOGGER.info(                f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')        if self.epochs < start_epoch:            LOGGER.info(                f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")            self.epochs += ckpt['epoch']  # finetune additional epochs        self.best_fitness = best_fitness        self.start_epoch = start_epoch

3.运行代码

没有在中断的train49训练，新开了一个文件夹，但是实现了功能

重要提示

训练完成后请把所有代码复原！！！
训练完成后请把所有代码复原！！！
训练完成后请把所有代码复原！！！

来源地址：https://blog.csdn.net/qq_43471945/article/details/129859047