超详细||YOLOv8基础教程（环境搭建，训练，测试，部署看一篇就够）（在推理视频中添加FPS信息）

一、YOLOv8环境搭建

这篇文章将跳过基础的深度学习环境的搭建，如果没有完成的可以看我的这篇博客：超详细||深度学习环境搭建记录cuda+anaconda+pytorch+pycharm-CSDN博客

1. 在github上下载源码：

GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

2. 安装ultralytics（YOLOv8改名为ultralytics）

这里有两种方式安装ultralytics

• 直接使用CLI

pip install ultralytics

• 使用requirements.txt安装，这种方法是在上面下载的源码处安装，方便对yolov8进行改进

cd ultralytics
pip install -r requirements.txt

3. 安装wandb

pip install wandb

登录自己的wandb账号

wandb login

二、开始训练

1. 构建数据集

数据集要严格按照下面的目录格式，image的格式为jpg，label的格式为txt，对应的image和label的名字要一致

Dataset└─images└─train└─val└─labels└─train└─val

2. 创建一个dataset.yaml文件

更换自己的image train和image val的地址，labels地址不用，它会自动索引

将classes改为自己的类别，从0开始

path: ../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)# Classes
names:0: person1: bicycle2: car3: motorcycle4: airplane5: bus6: train7: truck8: boat

3. 新建一个train.py，修改相关参数，运行即可开始训练

from ultralytics import YOLOif __name__ == '__main__':# Load a modelmodel = YOLO(r'\ultralytics\detection\yolov8n\yolov8n.yaml')  # 不使用预训练权重训练# model = YOLO(r'yolov8p.yaml').load("yolov8n.pt")  # 使用预训练权重训练# Trainparameters ----------------------------------------------------------------------------------------------model.train(data=r'\ultralytics\detection\dataset\appledata.yaml',epochs= 30 , # (int) number of epochs to train forpatience= 50 , # (int) epochs to wait for no observable improvement for early stopping of trainingbatch= 8 , # (int) number of images per batch (-1 for AutoBatch)imgsz= 320 , # (int) size of input images as integer or w,hsave= True , # (bool) save train checkpoints and predict resultssave_period= -1, # (int) Save checkpoint every x epochs (disabled if < 1)cache= False , # (bool) True/ram, disk or False. Use cache for data loadingdevice= 0 , # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpuworkers= 16 , # (int) number of worker threads for data loading (per RANK if DDP)project= 'result', # (str, optional) project namename= 'yolov8n' ,# (str, optional) experiment name, results saved to 'project/name' directoryexist_ok= False , # (bool) whether to overwrite existing experimentpretrained= False , # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)optimizer= 'SGD',  # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]verbose= True ,# (bool) whether to print verbose outputseed= 0 , # (int) random seed for reproducibilitydeterministic= True , # (bool) whether to enable deterministic modesingle_cls= True , # (bool) train multi-class data as single-classrect= False  ,# (bool) rectangular training if mode='train' or rectangular validation if mode='val'cos_lr= False , # (bool) use cosine learning rate schedulerclose_mosaic= 0,  # (int) disable mosaic augmentation for final epochsresume= False , # (bool) resume training from last checkpointamp= False,  # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP checkfraction= 1.0 , # (float) dataset fraction to train on (default is 1.0, all images in train set)profile= False,  # (bool) profile ONNX and TensorRT speeds during training for loggers# Segmentationoverlap_mask= True , # (bool) masks should overlap during training (segment train only)mask_ratio= 4,  # (int) mask downsample ratio (segment train only)# Classificationdropout= 0.0,  # (float) use dropout regularization (classify train only)# Hyperparameters ----------------------------------------------------------------------------------------------lr0=0.01,  # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)lrf=0.01,  # (float) final learning rate (lr0 * lrf)momentum=0.937,  # (float) SGD momentum/Adam beta1weight_decay=0.0005,  # (float) optimizer weight decay 5e-4warmup_epochs=3.0,  # (float) warmup epochs (fractions ok)warmup_momentum=0.8,  # (float) warmup initial momentumwarmup_bias_lr=0.1,  # (float) warmup initial bias lrbox=7.5,  # (float) box loss gaincls=0.5,  # (float) cls loss gain (scale with pixels)dfl=1.5,  # (float) dfl loss gainpose=12.0,  # (float) pose loss gainkobj=1.0,  # (float) keypoint obj loss gainlabel_smoothing=0.0,  # (float) label smoothing (fraction)nbs=64,  # (int) nominal batch sizehsv_h=0.015,  # (float) image HSV-Hue augmentation (fraction)hsv_s=0.7,  # (float) image HSV-Saturation augmentation (fraction)hsv_v=0.4,  # (float) image HSV-Value augmentation (fraction)degrees=0.0,  # (float) image rotation (+/- deg)translate=0.1,  # (float) image translation (+/- fraction)scale=0.5,  # (float) image scale (+/- gain)shear=0.0,  # (float) image shear (+/- deg)perspective=0.0,  # (float) image perspective (+/- fraction), range 0-0.001flipud=0.0,  # (float) image flip up-down (probability)fliplr=0.5,  # (float) image flip left-right (probability)mosaic=1.0,  # (float) image mosaic (probability)mixup=0.0,  # (float) image mixup (probability)copy_paste=0.0,  # (float) segment copy-paste (probability))

三、测试与验证

1. 新建一个test.py, 这个可以打印网路信息，参数量以及FLOPs，还有每一层网络的信息

from ultralytics import YOLOif __name__ == '__main__':# Load a modelmodel = YOLO(r'\ultralytics\detection\yolov8n\yolov8n.yaml')  # build a new model from YAMLmodel.info()

2. 新建一个val.py，这个可以打印模型在验证集上的结果，如mAP，推理速度等

from ultralytics import YOLOif __name__ == '__main__':# Load a modelmodel = YOLO(r'\ultralytics\detection\yolov8n\result\yolov8n4\weights\best.pt')  # build a new model from YAML# Validate the modelmodel.val(val=True,  # (bool) validate/test during trainingdata=r'\ultralytics\detection\dataset\appledata.yaml',split='val',  # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'batch=1,  # 测试速度时一般设置为 1 ，设置越大速度越快。   (int) number of images per batch (-1 for AutoBatch)imgsz=320,  # (int) size of input images as integer or w,hdevice=0,  # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpuworkers=8,  # (int) number of worker threads for data loading (per RANK if DDP)save_json=False,  # (bool) save results to JSON filesave_hybrid=False,  # (bool) save hybrid version of labels (labels + additional predictions)conf=0.001,  # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)iou=0.7,  # (float) intersection over union (IoU) threshold for NMSproject='val',  # (str, optional) project namename='',  # (str, optional) experiment name, results saved to 'project/name' directorymax_det=300,  # (int) maximum number of detections per imagehalf=True,  # (bool) use half precision (FP16)dnn=False,  # (bool) use OpenCV DNN for ONNX inferenceplots=True,  # (bool) save plots during train/val)

3. 新建一个predict.py，这个可以根据训练好的权重文件进行推理，权重文件格式支持pt，onnx等，支持图片，视频，摄像头等进行推理

from ultralytics import YOLOif __name__ == '__main__':# Load a modelmodel = YOLO(r'\deploy\yolov8n.pt')  # pretrained YOLOv8n modelmodel.predict(source=r'\deploy\output_video.mp4',save=False,  # save predict resultsimgsz=320,  # (int) size of input images as integer or w,hconf=0.25,  # object confidence threshold for detection (default 0.25 predict, 0.001 val)iou=0.7,  # # intersection over union (IoU) threshold for NMSshow=True,  # show results if possibleproject='',  # (str, optional) project namename='',  # (str, optional) experiment name, results saved to 'project/name' directorysave_txt=False,  # save results as .txt filesave_conf=True,  # save results with confidence scoressave_crop=False,  # save cropped images with resultsshow_labels=True,  # show object labels in plotsshow_conf=True,  # show object confidence scores in plotsvid_stride=1,  # video frame-rate strideline_width=1,  # bounding box thickness (pixels)visualize=False,  # visualize model featuresaugment=False,  # apply image augmentation to prediction sourcesagnostic_nms=False,  # class-agnostic NMSretina_masks=False,  # use high-resolution segmentation masksboxes=True,  # Show boxes in segmentation predictions)

四、onnx模型部署

1. 新建一个export.py，将pt文件转化为onnx文件

from ultralytics import YOLO# Load a model
model = YOLO('/ultralytics/weight file/yolov8n.pt')  # load a custom trained model# Export the model
model.export(format='onnx')

2. 将onnx文件添加到之前提到的predict.py中，进行推理。

3. 如果想在推理的视频中添加FPS信息，请把ultralytics/engine/predictor.py替换为下面的代码。

# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.Usage - sources:$ yolo mode=predict model=yolov8n.pt source=0                               # webcamimg.jpg                         # imagevid.mp4                         # videoscreen                          # screenshotpath/                           # directorylist.txt                        # list of imageslist.streams                    # list of streams'path/*.jpg'                    # glob'https://youtu.be/Zgi9g1ksQHc'  # YouTube'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streamUsage - formats:$ yolo mode=predict model=yolov8n.pt                 # PyTorchyolov8n.torchscript        # TorchScriptyolov8n.onnx               # ONNX Runtime or OpenCV DNN with dnn=Trueyolov8n_openvino_model     # OpenVINOyolov8n.engine             # TensorRTyolov8n.mlmodel            # CoreML (macOS-only)yolov8n_saved_model        # TensorFlow SavedModelyolov8n.pb                 # TensorFlow GraphDefyolov8n.tflite             # TensorFlow Liteyolov8n_edgetpu.tflite     # TensorFlow Edge TPUyolov8n_paddle_model       # PaddlePaddle
"""
import platform
from pathlib import Pathimport cv2
import numpy as np
import torchfrom ultralytics.cfg import get_cfg
from ultralytics.data import load_inference_source
from ultralytics.data.augment import LetterBox, classify_transforms
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops
from ultralytics.utils.checks import check_imgsz, check_imshow
from ultralytics.utils.files import increment_path
from ultralytics.utils.torch_utils import select_device, smart_inference_modeSTREAM_WARNING = """WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,causing potential out-of-memory errors for large sources or long-running streams/videos.Usage:results = model(source=..., stream=True)  # generator of Results objectsfor r in results:boxes = r.boxes  # Boxes object for bbox outputsmasks = r.masks  # Masks object for segment masks outputsprobs = r.probs  # Class probabilities for classification outputs
"""class BasePredictor:"""BasePredictorA base class for creating predictors.Attributes:args (SimpleNamespace): Configuration for the predictor.save_dir (Path): Directory to save results.done_warmup (bool): Whether the predictor has finished setup.model (nn.Module): Model used for prediction.data (dict): Data configuration.device (torch.device): Device used for prediction.dataset (Dataset): Dataset used for prediction.vid_path (str): Path to video file.vid_writer (cv2.VideoWriter): Video writer for saving video output.data_path (str): Path to data."""def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):"""Initializes the BasePredictor class.Args:cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.overrides (dict, optional): Configuration overrides. Defaults to None."""self.args = get_cfg(cfg, overrides)self.save_dir = self.get_save_dir()if self.args.conf is None:self.args.conf = 0.25  # default conf=0.25self.done_warmup = Falseif self.args.show:self.args.show = check_imshow(warn=True)# Usable if setup is doneself.model = Noneself.data = self.args.data  # data_dictself.imgsz = Noneself.device = Noneself.dataset = Noneself.vid_path, self.vid_writer = None, Noneself.plotted_img = Noneself.data_path = Noneself.source_type = Noneself.batch = Noneself.results = Noneself.transforms = Noneself.callbacks = _callbacks or callbacks.get_default_callbacks()self.txt_path = Nonecallbacks.add_integration_callbacks(self)def get_save_dir(self):project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.taskname = self.args.name or f'{self.args.mode}'return increment_path(Path(project) / name, exist_ok=self.args.exist_ok)def preprocess(self, im):"""Prepares input image before inference.Args:im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list."""not_tensor = not isinstance(im, torch.Tensor)if not_tensor:im = np.stack(self.pre_transform(im))im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)im = np.ascontiguousarray(im)  # contiguousim = torch.from_numpy(im)img = im.to(self.device)img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32if not_tensor:img /= 255  # 0 - 255 to 0.0 - 1.0return imgdef inference(self, im, *args, **kwargs):visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem,mkdir=True) if self.args.visualize and (not self.source_type.tensor) else Falsereturn self.model(im, augment=self.args.augment, visualize=visualize)def pre_transform(self, im):"""Pre-transform input image before inference.Args:im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.Return: A list of transformed imgs."""same_shapes = all(x.shape == im[0].shape for x in im)auto = same_shapes and self.model.ptreturn [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]def write_results(self, idx, results, batch):"""Write inference results to a file or directory."""p, im, _ = batchlog_string = ''if len(im.shape) == 3:im = im[None]  # expand for batch dimif self.source_type.webcam or self.source_type.from_img or self.source_type.tensor:  # batch_size >= 1log_string += f'{idx}: 'frame = self.dataset.countelse:frame = getattr(self.dataset, 'frame', 0)self.data_path = pself.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')log_string += '%gx%g ' % im.shape[2:]  # print stringresult = results[idx]log_string += result.verbose()if self.args.save or self.args.show:  # Add bbox to imageplot_args = {'line_width': self.args.line_width,'boxes': self.args.boxes,'conf': self.args.show_conf,'labels': self.args.show_labels}if not self.args.retina_masks:plot_args['im_gpu'] = im[idx]self.plotted_img = result.plot(**plot_args)# Writeif self.args.save_txt:result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)if self.args.save_crop:result.save_crop(save_dir=self.save_dir / 'crops',file_name=self.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}'))return log_stringdef postprocess(self, preds, img, orig_imgs):"""Post-processes predictions for an image and returns them."""return predsdef __call__(self, source=None, model=None, stream=False, *args, **kwargs):"""Performs inference on an image or stream."""self.stream = streamif stream:return self.stream_inference(source, model, *args, **kwargs)else:return list(self.stream_inference(source, model, *args, **kwargs))  # merge list of Result into onedef predict_cli(self, source=None, model=None):"""Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode."""gen = self.stream_inference(source, model)for _ in gen:  # running CLI inference without accumulating any outputs (do not modify)passdef setup_source(self, source):"""Sets up source and inference mode."""self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2)  # check image sizeself.transforms = getattr(self.model.model, 'transforms', classify_transforms(self.imgsz[0])) if self.args.task == 'classify' else Noneself.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)self.source_type = self.dataset.source_typeif not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or  # streamslen(self.dataset) > 1000 or  # imagesany(getattr(self.dataset, 'video_flag', [False]))):  # videosLOGGER.warning(STREAM_WARNING)self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs@smart_inference_mode()def stream_inference(self, source=None, model=None, *args, **kwargs):"""Streams real-time inference on camera feed and saves results to file."""if self.args.verbose:LOGGER.info('')# Setup modelif not self.model:self.setup_model(model)# Setup source every time predict is calledself.setup_source(source if source is not None else self.args.source)# Check if save_dir/ label file existsif self.args.save or self.args.save_txt:(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)# Warmup modelif not self.done_warmup:self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))self.done_warmup = Trueself.seen, self.windows, self.batch, self.profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile())self.run_callbacks('on_predict_start')for batch in self.dataset:self.run_callbacks('on_predict_batch_start')self.batch = batchpath, im0s, vid_cap, s = batch# Preprocesswith self.profilers[0]:im = self.preprocess(im0s)# Inferencewith self.profilers[1]:preds = self.inference(im, *args, **kwargs)# Postprocesswith self.profilers[2]:self.results = self.postprocess(preds, im, im0s)self.run_callbacks('on_predict_postprocess_end')# Visualize, save, write resultsn = len(im0s)for i in range(n):self.seen += 1self.results[i].speed = {'preprocess': self.profilers[0].dt * 1E3 / n,'inference': self.profilers[1].dt * 1E3 / n,'postprocess': self.profilers[2].dt * 1E3 / n}p, im0 = path[i], None if self.source_type.tensor else im0s[i].copy()p = Path(p)if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:s += self.write_results(i, self.results, (p, im, im0))if self.args.save or self.args.save_txt:self.results[i].save_dir = self.save_dir.__str__()if self.args.show and self.plotted_img is not None:self.show(p)if self.args.save and self.plotted_img is not None:self.save_preds(vid_cap, i, str(self.save_dir / p.name))self.run_callbacks('on_predict_batch_end')yield from self.results# Print time (inference-not only)if self.args.verbose:LOGGER.info(f'{s}{(self.profilers[0].dt+self.profilers[1].dt+self.profilers[2].dt) * 1E3:.2f}ms')# Release assetsif isinstance(self.vid_writer[-1], cv2.VideoWriter):self.vid_writer[-1].release()  # release final video writer# Print resultsif self.args.verbose and self.seen:t = tuple(x.t / self.seen * 1E3 for x in self.profilers)  # speeds per imageLOGGER.info(f'Speed: %.2fms preprocess, %.2fms inference, %.2fms postprocess per image at shape 'f'{(1, 3, *im.shape[2:])}' % t)if self.args.save or self.args.save_txt or self.args.save_crop:nl = len(list(self.save_dir.glob('labels/*.txt')))  # number of labelss = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")self.run_callbacks('on_predict_end')def setup_model(self, model, verbose=True):"""Initialize YOLO model with given parameters and set it to evaluation mode."""self.model = AutoBackend(model or self.args.model,device=select_device(self.args.device, verbose=verbose),dnn=self.args.dnn,data=self.args.data,fp16=self.args.half,fuse=True,verbose=verbose)self.device = self.model.device  # update deviceself.args.half = self.model.fp16  # update halfself.model.eval()def show(self, p):"""Display an image in a window using OpenCV imshow()."""im0 = self.plotted_img#--------------------后添加-----------------------------str_FPS = "FPS: %.2f" % (1. / (self.profilers[0].dt + self.profilers[1].dt + self.profilers[2].dt))cv2.putText(im0, str_FPS, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 3)# --------------------后添加-----------------------------if platform.system() == 'Linux' and p not in self.windows:self.windows.append(p)cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])cv2.imshow(str(p), im0)cv2.waitKey(500 if self.batch[3].startswith('image') else 1)  # 1 milliseconddef save_preds(self, vid_cap, idx, save_path):"""Save video predictions as mp4 at specified path."""im0 = self.plotted_img# Save imgsif self.dataset.mode == 'image':cv2.imwrite(save_path, im0)else:  # 'video' or 'stream'if self.vid_path[idx] != save_path:  # new videoself.vid_path[idx] = save_pathif isinstance(self.vid_writer[idx], cv2.VideoWriter):self.vid_writer[idx].release()  # release previous video writerif vid_cap:  # videofps = int(vid_cap.get(cv2.CAP_PROP_FPS))  # integer required, floats produce error in MP4 codecw = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))else:  # streamfps, w, h = 30, im0.shape[1], im0.shape[0]suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi'fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG'save_path = str(Path(save_path).with_suffix(suffix))self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))self.vid_writer[idx].write(im0)def run_callbacks(self, event: str):"""Runs all registered callbacks for a specific event."""for callback in self.callbacks.get(event, []):callback(self)def add_callback(self, event: str, func):"""Add callback"""self.callbacks[event].append(func)