yolov5-7.0

### YOLOv5 7.0 Installation Guide and Usage Tutorial #### Prerequisites Before installing YOLOv5 version 7.0, ensure that Python 3.x is installed along with pip package manager. Additionally, it's recommended to use a virtual environment for managing dependencies effectively. #### Installing Dependencies To set up the necessary libraries and tools required by YOLOv5, execute the following commands within the project directory: ```bash pip install -r requirements.txt ``` This command installs all dependency packages specified in `requirements.txt` file which includes PyTorch among other essential components[^1]. #### Cloning Repository Obtain the latest source code of YOLOv5 from its official GitHub repository via cloning: ```bash git clone https://github.com/ultralytics/yolov5.git cd yolov5 ``` The above steps fetches the entire project including pre-trained models used during inference tasks. #### Running Inference Using Pre-Trained Models Once everything has been configured properly, running object detection on images or videos becomes straightforward through these lines of python script provided as part of documentation examples: ```python from pathlib import Path import torch from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams from utils.general import (LOGGER, check_file, check_img_size, check_imshow, colorstr, increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, time_sync weights = 'yolov5s.pt' # model path or triton URL source = 'data/images/bus.jpg' 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_conf=False # save confidences in --save-txt labels save_crop=False # save cropped prediction boxes nosave=True # 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='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 # Initialize with torch.no_grad(): weights, imgsz = str(weights), tuple(imgsz) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn) stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size # Half half &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 supported on limited backends with CUDA if pt or jit: model.model.half() if half else model.model.float() # Data loader setup omitted here... # Run inference... ``` This snippet demonstrates how one might perform an object-detection task using pretrained weights available inside the cloned repo. #### Training Custom Dataset Training custom datasets involves preparing data according to specific formats expected by YOLOv5 framework followed by adjusting configuration files accordingly before initiating training process described thoroughly at [official README](https://github.com/ultralytics/yolov5/blob/master/README.md).

yolov5-7.0是一个用于目标检测的深度学习模型。它是YOLO（You Only Look Once）系列模型的最新版本之一。yolov5-7.0通过对输入图像进行单次前向传递来实现目标检测，可以在图像中同时检测出多个不同类别的目标。 相较于早期的版本，yolov5-7.0具有更高的检测精度和更快的推理速度。它采用了一系列的改进措施，包括更深的网络结构、更高分辨率的输入图像、更多的训练数据等。此外，yolov5-7.0还引入了一种新的训练方法，称为Self-training，可以通过在无标签数据上进行自我训练来提高模型性能。