YOLOv8 脸部识别是一个基于YOLOv8算法的人脸检测项目，旨在实现快速、准确地检测图像和视频中的人脸。该项目是对YOLOv8算法的扩展和优化，专门用于人脸检测任务。
YOLOv8是一种基于深度学习的目标检测算法，通过将目标检测问题转化为一个回归问题，可以实现实时的目标检测。YOLOv8 Face项目在YOLOv8的基础上进行了改进，使其更加适用于人脸检测。以下是YOLOv8 Face项目的一些特点和关键技术：
1.高准确性：YOLOv8
Face采用了一系列的优化策略，包括网络结构的设计、数据增强和训练技巧等，从而提高了模型的准确性。它能够精确地检测出各种不同姿态、光照和遮挡条件下的人脸。
2.实时性能：YOLOv8Face具有较高的实时性能，可以在实时图像和视频流中快速检测人脸。它采用了一种轻量级的网络结构和高效的推理算法，以实现实时的人脸检测。
3.多尺度检测：为了适应不同大小和尺度的人脸，YOLOv8 Face使用了多尺度检测技术。通过在不同尺度下进行检测，可以提高模型对小尺寸人脸的检测能力。
4.数据增强：YOLOv8 Face使用了各种数据增强技术，如随机裁剪、旋转和缩放等，以增加训练数据的多样性和丰富性。这有助于提高模型的泛化能力和鲁棒性。
5.高效推理：为了提高推理效率，YOLOv8 Face使用了一些优化技术，如模型压缩、量化和推理引擎的优化等。这使得模型可以在嵌入式设备和移动端实现快速的人脸检测。
代码运行
数据准备
- 下载WIDERFace数据集
- 从Google Drive下载注释文件
- 进入data文件夹
4.运行python3 train2yolo.py /path/to/original/widerface/train` [/path/to/save/widerface/train]，将训练集转换为YOLOv5格式
5.运行python3 val2yolo.py /path/to/original/widerface [/path/to/save/widerface/val]，将验证集转换为YOLOv5格式
训练
6.运行CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models'，进行训练
WIDERFace评估
- 进入
widerface_evaluate文件夹 - 运行
python3 evaluation.py，进行评估
demo代码
运行下列demo示例，可以帮助我们推理出结果！！！！！！1
import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \ strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized def detect(save_img=False): source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size print('weights: ', weights) webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://')) # Directories save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Initialize set_logging() device = select_device(opt.device) half = device.type != 'cpu' # half precision only supported on CUDA # Load model model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size if half: model.half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() # Set Dataloader vid_path, vid_writer = None, None if webcam: view_img = True cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz) else: save_img = True dataset = LoadImages(source, img_size=imgsz) # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # Run inference t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() pred = model(img, augment=opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t2 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) # Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f'{n} {names[int(c)]}s, ' # add to string # Write results for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or view_img: # Add bbox to image label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) # Print time (inference + NMS) print(f'{s}Done. ({t2 - t1:.3f}s)') # Stream results if view_img: cv2.imshow(str(p), im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) else: # 'video' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer fourcc = 'mp4v' # output video codec fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") print(f'Done. ({time.time() - t0:.3f}s)') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='./weights/yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') opt = parser.parse_args() print(opt) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: detect() strip_optimizer(opt.weights) else: detect()
总之，YOLOv8 Face项目是一个基于YOLOv8算法的人脸检测项目，具有高准确性、实时性能和多尺度检测等特点。它可以广泛应用于人脸识别、人脸表情分析、人脸属性识别等领域，为人脸相关的应用提供强大的支持。
