(base) zuochunxue@tyw2:~$ yolo task=detect mode=train model=yolo11x.yaml data=FishEyeData01.yaml epochs=100 batch=64 Ultralytics 8.3.104 🚀 Python-3.12.7 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 4070 Ti SUPER, 16064MiB) engine/trainer: task=detect, mode=train, model=yolo11x.yaml, data=FishEyeData01.yaml, epochs=100, time=None, patience=100, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train5, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train5 Traceback (most recent call last): File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 582, in get_dataset data = check_det_dataset(self.args.data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/data/utils.py", line 312, in check_det_dataset file = check_file(dataset) ^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/utils/checks.py", line 546, in check_file raise FileNotFoundError(f"'{file}' does not exist") FileNotFoundError: 'FishEyeData01.yaml' does not exist The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/anaconda3/bin/yolo", line 8, in <module> sys.exit(entrypoint()) ^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/cfg/__init__.py", line 987, in entrypoint getattr(model, mode)(**overrides) # default args from model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/engine/model.py", line 785, in train self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 137, in __init__ self.trainset, self.testset = self.get_dataset() ^^^^^^^^^^^^^^^^^^ File "/root/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 586, in get_dataset raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e RuntimeError: Dataset 'FishEyeData01.yaml' error ❌ 'FishEyeData01.yaml' does not exist

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlpackage # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle """ import argparse import csv import os import platform import sys from pathlib import Path import torch FILE = Path(file).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.plotting import Annotator, colors, save_one_box from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import ( LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh, ) from utils.torch_utils import select_device, smart_inference_mode # 新增：计算IOU函数 def calculate_iou(box1, box2): """计算两个边界框的IOU""" x1, y1, x2, y2 = box1 x1g, y1g, x2g, y2g = box2 # 计算交集区域 xA = max(x1, x1g) yA = max(y1, y1g) xB = min(x2, x2g) yB = min(y2, y2g) # 计算交集面积 inter_area = max(0, xB - xA + 1) max(0, yB - yA + 1) # 计算并集面积 box1_area = (x2 - x1 + 1) * (y2 - y1 + 1) box2_area = (x2g - x1g + 1) * (y2g - y1g + 1) union_area = float(box1_area + box2_area - inter_area) # 计算IOU iou = inter_area / union_area return iou # 新增：计算准确率函数 def calculate_accuracy(gt_labels, pred_detections, iou_threshold=0.5): """计算目标检测的准确率""" correct_predictions = 0 total_gt_objects = 0 total_pred_objects = 0 for img_name in gt_labels: if img_name not in pred_detections: continue gt_boxes = gt_labels[img_name] pred_boxes = pred_detections[img_name] total_gt_objects += len(gt_boxes) total_pred_objects += len(pred_boxes) # 标记已匹配的真实标签 gt_matched = [False] * len(gt_boxes) for pred_box in pred_boxes: pred_class, pred_bbox, pred_conf = pred_box best_iou = 0 best_gt_idx = -1 # 寻找最佳匹配的真实标签 for i, gt_box in enumerate(gt_boxes): gt_class, gt_bbox = gt_box if gt_matched[i]: continue iou = calculate_iou(pred_bbox, gt_bbox) if iou > best_iou and pred_class == gt_class: best_iou = iou best_gt_idx = i # 如果IOU超过阈值且类别正确，则计为正确预测 if best_gt_idx != -1 and best_iou >= iou_threshold: correct_predictions += 1 gt_matched[best_gt_idx] = True # 避免除零错误 if total_gt_objects == 0: return 0.0 # 计算准确率 return correct_predictions / total_gt_objects @smart_inference_mode() def run( weights=ROOT / "yolov5s.pt", # model path or triton URL source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) data=ROOT / "data/coco128.yaml", # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to .txt save_format=0, # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC) save_csv=False, # save results in CSV format save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / "runs/detect", # save results to project/name name="exp", # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride gt_dir="", # 新增：真实标签目录 eval_interval=10, # 新增：评估间隔帧数 ): """ Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc. Args: weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'. source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index. Default is 'data/images'. data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'. imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640). conf_thres (float): Confidence threshold for detections. Default is 0.25. iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45. max_det (int): Maximum number of detections per image. Default is 1000. device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which uses the best available device. view_img (bool): If True, display inference results using OpenCV. Default is False. save_txt (bool): If True, save results in a text file. Default is False. save_format (int): Whether to save boxes coordinates in YOLO format or Pascal-VOC format. Default is 0. save_csv (bool): If True, save results in a CSV file. Default is False. save_conf (bool): If True, include confidence scores in the saved results. Default is False. save_crop (bool): If True, save cropped prediction boxes. Default is False. nosave (bool): If True, do not save inference images or videos. Default is False. classes (list[int]): List of classes to filter detections by. Default is None. agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False. augment (bool): If True, use augmented inference. Default is False. visualize (bool): If True, visualize feature maps. Default is False. update (bool): If True, update all models' weights. Default is False. project (str | Path): Directory to save results. Default is 'runs/detect'. name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'. exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is False. line_thickness (int): Thickness of bounding box lines in pixels. Default is 3. hide_labels (bool): If True, do not display labels on bounding boxes. Default is False. hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False. half (bool): If True, use FP16 half-precision inference. Default is False. dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False. vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1. gt_dir (str): 新增：真实标签目录路径 eval_interval (int): 新增：每隔多少帧计算一次准确率 Returns: None """ source = str(source) save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(("rtsp://", "rtmp://", "https://", "https://")) webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] bs, [None] * bs # 新增：加载真实标签数据 gt_labels = {} if gt_dir: gt_dir = Path(gt_dir) for txt_file in gt_dir.glob(".txt"): img_name = txt_file.stem gt_labels[img_name] = [] with open(txt_file, "r") as f: for line in f: parts = line.strip().split() if len(parts) >= 5: cls = int(parts[0]) # 将YOLO格式转换为xyxy格式 x, y, w, h = map(float, parts[1:5]) # 假设真实标签对应的图像尺寸与输入图像一致 x1 = (x - w/2) imgsz[1] y1 = (y - h/2) * imgsz[0] x2 = (x + w/2) * imgsz[1] y2 = (y + h/2) * imgsz[0] gt_labels[img_name].append((cls, (x1, y1, x2, y2))) # 新增：收集预测结果 pred_detections = {} frame_count = 0 accuracy = 0.0 # 初始化准确率 # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, imgsz)) # warmup seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim if model.xml and im.shape[0] > 1: ims = torch.chunk(im, im.shape[0], 0) # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False if model.xml and im.shape[0] > 1: pred = None for image in ims: if pred is None: pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) else: pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) pred = [pred, None] else: pred = model(im, augment=augment, visualize=visualize) # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Define the path for the CSV file csv_path = save_dir / "predictions.csv" # Create or append to the CSV file def write_to_csv(image_name, prediction, confidence): """Writes prediction data for an image to a CSV file, appending if the file exists.""" data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence} file_exists = os.path.isfile(csv_path) with open(csv_path, mode="a", newline="") as f: writer = csv.DictWriter(f, fieldnames=data.keys()) if not file_exists: writer.writeheader() writer.writerow(data) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f"{i}: " else: p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt s += "{:g}x{:g} ".format(im.shape[2:]) # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, 5].unique(): n = (det[:, 5] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for xyxy, conf, cls in reversed(det): c = int(cls) # integer class label = names[c] if hide_conf else f"{names[c]}" confidence = float(conf) confidence_str = f"{confidence:.2f}" if save_csv: write_to_csv(p.name, label, confidence_str) if save_txt: # Write to file if save_format == 0: coords = ( (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() ) # normalized xywh else: coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist() # xyxy line = (cls, coords, conf) if save_conf else (cls, coords) # label format with open(f"{txt_path}.txt", "a") as f: f.write(("%g " len(line)).rstrip() % line + "\n") if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop: save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # 新增：收集预测结果 img_name = p.stem pred_detections[img_name] = [] if len(det): for xyxy, conf, cls in det: c = int(cls) x1, y1, x2, y2 = map(int, xyxy) pred_detections[img_name].append((c, (x1, y1, x2, y2), float(conf))) # 新增：定期计算准确率并显示 frame_count += 1 if gt_dir and frame_count % eval_interval == 0: accuracy = calculate_accuracy(gt_labels, pred_detections) if save_img or view_img: accuracy_text = f"Accuracy: {accuracy:.2f}" annotator.text((10, 30), accuracy_text, txt_color=(255, 255, 255)) im0 = annotator.result() # Stream results im0 = annotator.result() if view_img: if platform.system() == "Linux" and p not in windows: 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(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video 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)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix(".mp4")) # force .mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc("mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt 1e3:.1f}ms") # 新增：在终端输出最终准确率 if gt_dir: accuracy = calculate_accuracy(gt_labels, pred_detections) LOGGER.info(f"Overall Accuracy: {accuracy:.4f}") # Print results t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, imgsz)}" % t) 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 "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): """ Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations. Args: --weights (str | list[str], optional): Model path or triton URL. Defaults to ROOT / 'yolov5s.pt'. --source (str, optional): File/dir/URL/glob/screen/0(webcam). Defaults to ROOT / 'data/images'. --data (str, optional): Dataset YAML path. Provides dataset configuration information. --imgsz (list[int], optional): Inference size (height, width). Defaults to [640]. --conf-thres (float, optional): Confidence threshold. Defaults to 0.25. --iou-thres (float, optional): NMS IoU threshold. Defaults to 0.45. --max-det (int, optional): Maximum number of detections per image. Defaults to 1000. --device (str, optional): CUDA device, i.e. 0 or 0,1,2,3 or cpu. Defaults to "". --view-img (bool, optional): Flag to display results. Default is False. --save-txt (bool, optional): Flag to save results to .txt files. Default is False. --save-format (int, optional): Whether to save boxes coordinates in YOLO format or Pascal-VOC format. Default is 0. --save-csv (bool, optional): Flag to save results in CSV format. Default is False. --save-conf (bool, optional): Flag to save confidences in labels saved via --save-txt. Default is False. --save-crop (bool, optional): Flag to save cropped prediction boxes. Default is False. --nosave (bool, optional): Flag to prevent saving images/videos. Default is False. --classes (list[int], optional): List of classes to filter results by. Default is None. --agnostic-nms (bool, optional): Flag for class-agnostic NMS. Default is False. --augment (bool, optional): Flag for augmented inference. Default is False. --visualize (bool, optional): Flag for visualizing features. Default is False. --update (bool, optional): Flag to update all models in the model directory. Default is False. --project (str, optional): Directory to save results. Default is ROOT / 'runs/detect'. --name (str, optional): Sub-directory name for saving results within --project. Default is 'exp'. --exist-ok (bool, optional): Flag to allow overwriting if the project/name already exists. Default is False. --line-thickness (int, optional): Thickness (in pixels) of bounding boxes. Default is 3. --hide-labels (bool, optional): Flag to hide labels in the output. Default is False. --hide-conf (bool, optional): Flag to hide confidences in the output. Default is False. --half (bool, optional): Flag to use FP16 half-precision inference. Default is False. --dnn (bool, optional): Flag to use OpenCV DNN for ONNX inference. Default is False. --vid-stride (int, optional): Video frame-rate stride. Default is 1. --gt-dir (str, optional): 新增：真实标签目录路径 --eval-interval (int, optional): 新增：每隔多少帧计算一次准确率 Returns: argparse.Namespace: Parsed command-line arguments as an argparse.Namespace object. """ parser = argparse.ArgumentParser() parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL") parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") 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="show results") parser.add_argument("--save-txt", action="store_true", help="save results to .txt") parser.add_argument( "--save-format", type=int, default=0, help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC", ) parser.add_argument("--save-csv", action="store_true", help="save results in CSV format") parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") parser.add_argument("--nosave", action="store_true", help="do not save images/videos") parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 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("--visualize", action="store_true", help="visualize features") parser.add_argument("--update", action="store_true", help="update all models") parser.add_argument("--project", default=ROOT / "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") parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") # 新增参数 parser.add_argument("--gt-dir", type=str, default="", help="ground truth labels directory") parser.add_argument("--eval-interval", type=int, default=10, help="evaluate accuracy every N frames") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """ Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running. Args: opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. Returns: None """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if name == "main": opt = parse_opt() main(opt)代码如上。yolov5在detect.py得到有类别和置信度标注的视频和图片，请问我如何操作，才能在有类别和置信度标注的视频和图片的基础上，在视频或图片中显示识别准确率Accuracy。请给出修改后的完整代码（尽量少修改，不要改变代码的其他地方），要求直接在vscode点击运行即可生成显示识别准确率Accuracy的视频或图片

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Traceback (most recent call last): File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\train.py", line 19, in <module> model = YOLO('ultralytics/cfg/models/llw/yolo11-HS-FPN.yaml') # YOLO11 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\models\yolo\model.py", line 23, in init super().init(model=model, task=task, verbose=verbose) File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\engine\model.py", line 143, in init self._new(model, task=task, verbose=verbose) File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\engine\model.py", line 251, in _new self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\tasks.py", line 446, in init m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(2, ch, s, s))]) # forward ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\tasks.py", line 443, in _forward return self.forward(x)[0] if isinstance(m, SEGMENT_CLASS + POSE_CLASS + OBB_CLASS) else self.forward(x) ^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\tasks.py", line 166, in forward return self.predict(x, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\tasks.py", line 184, in predict return self._predict_once(x, profile, visualize, embed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\tasks.py", line 217, in _predict_once x = m(x) # run ^^^^ File "E:\Anaconda\envs\yolo\Lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Anaconda\envs\yolo\Lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\extra_modules\HSFPN.py", line 133, in forward spatial = self.spatial(x) # output of spatial path ^^^^^^^^^^^^^^^ File "E:\Anaconda\envs\yolo\Lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Anaconda\envs\yolo\Lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\yolo12\ultralytics-yolo11-20250706\ultralytics-yolo11-main\ultralytics\nn\extra_modules\HSFPN.py", line 45, in forward idct = DCT.dct_2d(x, norm='ortho') ^^^ NameError: name 'DCT' is not defined. Did you mean: 'dct'?

# 对宽度（最后一个维度）做变换：x * dct_mat_W^T -> (B*C, H, W) * (W, W) -> (B*C, H, W) x = torch.matmul(x, dct_mat_W.t()) # 再对高度做变换：x * dct_mat_H -> (B*C, H, W) -> 转置高度和宽度？不，我们...

"E:\Program Files\anaconda3\envs\v8\python.exe" E:\Python_PyCharm\【大作业-09】YOLOv5实现手势识别\yolov5-hand-42\train.py wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information. wandb: Currently logged in as: 2358781902 (2358781902-) to https://api.wandb.ai. Use wandb login --relogin to force relogin train: weights=pretrained\yolov5s.pt, cfg=models\yolov5s.yaml, data=data\data.yaml, hyp=data\hyps\hyp.scratch.yaml, epochs=300, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=True, single_cls=False, adam=False, sync_bn=False, workers=0, project=runs\train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: skipping check (offline), for updates see https://github.com/ultralytics/yolov5 requirements: Pillow==8.4.0 not found and is required by YOLOv5, attempting auto-update... requirements: 'pip install Pillow==8.4.0' skipped (offline) YOLOv5 7e8b886 torch 2.4.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB) hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/ wandb: Tracking run with wandb version 0.19.8 wandb: Run data is saved locally in E:\Python_PyCharm\【大作业-09】YOLOv5实现手势识别\yolov5-hand-42\wandb\run-20250321_084633-k4djllk2 wandb: Run wandb offline to turn off syncing. wandb: Syncing run toasty-violet-1 wa

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