# Ultralytics YOLOv5 🚀, AGPL-3.0 license """ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5. Usage: import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo """ import torch def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): """ Creates or loads a YOLOv5 model, with options for pretrained weights and model customization. Args: name (str): Model name (e.g., 'yolov5s') or path to the model checkpoint (e.g., 'path/to/best.pt'). pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True. channels (int, optional): Number of input channels the model expects. Defaults to 3. classes (int, optional): Number of classes the model is expected to detect. Defaults to 80. autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for various input formats. Defaults to True. verbose (bool, optional): If True, prints detailed information during the model creation/loading process. Defaults to True. device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). If None, selects the best available device. Defaults to None. Returns: (DetectMultiBackend | AutoShape): The loaded YOLOv5 model, potentially wrapped with AutoShape if specified. Examples: ```python import torch from ultralytics import _create # Load an official YOLOv5s model with pretrained weights model = _create('yolov5s') # Load a custom model from a local checkpoint model = _create('path/to/custom_model.pt', pretrained=False) # Load a model with specific input channels and classes model = _create('yolov5s', channels=1, classes=10) ``` Notes: For more information on model loading and customization, visit the [YOLOv5 PyTorch Hub Documentation](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading). """ from pathlib import Path from models.common import AutoShape, DetectMultiBackend from models.experimental import attempt_load from models.yolo import ClassificationModel, DetectionModel, SegmentationModel from utils.downloads import attempt_download from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging from utils.torch_utils import select_device if not verbose: LOGGER.setLevel(logging.WARNING) check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop")) name = Path(name) path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path try: device = select_device(device) if pretrained and channels == 3 and classes == 80: try: model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model if autoshape: if model.pt and isinstance(model.model, ClassificationModel): LOGGER.warning( "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. " "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)." ) elif model.pt and isinstance(model.model, SegmentationModel): LOGGER.warning( "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. " "You will not be able to run inference with this model." ) else: model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS except Exception: model = attempt_load(path, device=device, fuse=False) # arbitrary model else: cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path model = DetectionModel(cfg, channels, classes) # create model if pretrained: ckpt = torch.load(attempt_download(path), map_location=device) # load csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect model.load_state_dict(csd, strict=False) # load if len(ckpt["model"].names) == classes: model.names = ckpt["model"].names # set class names attribute if not verbose: LOGGER.setLevel(logging.INFO) # reset to default return model.to(device) except Exception as e: help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help." raise Exception(s) from e def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None): """ Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification. Args: path (str): Path to the custom model file (e.g., 'path/to/model.pt'). autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model if True, enabling compatibility with various input types (default is True). _verbose (bool): If True, prints all informational messages to the screen; otherwise, operates silently (default is True). device (str | torch.device | None): Device to load the model on, e.g., 'cpu', 'cuda', torch.device('cuda:0'), etc. (default is None, which automatically selects the best available device). Returns: torch.nn.Module: A YOLOv5 model loaded with the specified parameters. Notes: For more details on loading models from PyTorch Hub: https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading Examples: ```python # Load model from a given path with autoshape enabled on the best available device model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # Load model from a local path without autoshape on the CPU device model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local', autoshape=False, device='cpu') ``` """ return _create(path, autoshape=autoshape, verbose=_verbose, device=device) def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping, verbosity, and device. Args: pretrained (bool): If True, loads pretrained weights into the model. Defaults to True. channels (int): Number of input channels for the model. Defaults to 3. classes (int): Number of classes for object detection. Defaults to 80. autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper to the model for various formats (file/URI/PIL/ cv2/np) and non-maximum suppression (NMS) during inference. Defaults to True. _verbose (bool): If True, prints detailed information to the screen. Defaults to True. device (str | torch.device | None): Specifies the device to use for model computation. If None, uses the best device available (i.e., GPU if available, otherwise CPU). Defaults to None. Returns: DetectionModel | ClassificationModel | SegmentationModel: The instantiated YOLOv5-nano model, potentially with pretrained weights and autoshaping applied. Notes: For further details on loading models from PyTorch Hub, refer to [PyTorch Hub models](https://pytorch.org/hub/ ultralytics_yolov5). Examples: ```python import torch from ultralytics import yolov5n # Load the YOLOv5-nano model with defaults model = yolov5n() # Load the YOLOv5-nano model with a specific device model = yolov5n(device='cuda') ``` """ return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping, verbosity, and device configuration. Args: pretrained (bool, optional): Flag to load pretrained weights into the model. Defaults to True. channels (int, optional): Number of input channels. Defaults to 3. classes (int, optional): Number of model classes. Defaults to 80. autoshape (bool, optional): Whether to wrap the model with YOLOv5's .autoshape() for handling various input formats. Defaults to True. _verbose (bool, optional): Flag to print detailed information regarding model loading. Defaults to True. device (str | torch.device | None, optional): Device to use for model computation, can be 'cpu', 'cuda', or torch.device instances. If None, automatically selects the best available device. Defaults to None. Returns: torch.nn.Module: The YOLOv5-small model configured and loaded according to the specified parameters. Example: ```python import torch # Load the official YOLOv5-small model with pretrained weights model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # Load the YOLOv5-small model from a specific branch model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # Load a custom YOLOv5-small model from a local checkpoint model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # Load a local YOLOv5-small model specifying source as local repository model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') ``` Notes: For more details on model loading and customization, visit the [YOLOv5 PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5). """ return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping, verbosity, and device. Args: pretrained (bool, optional): Whether to load pretrained weights into the model. Default is True. channels (int, optional): Number of input channels. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model for handling various input formats. Default is True. _verbose (bool, optional): Whether to print detailed information to the screen. Default is True. device (str | torch.device | None, optional): Device specification to use for model parameters (e.g., 'cpu', 'cuda'). Default is None. Returns: torch.nn.Module: The instantiated YOLOv5-medium model. Usage Example: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5m') # Load YOLOv5-medium from Ultralytics repository model = torch.hub.load('ultralytics/yolov5:master', 'yolov5m') # Load from the master branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m.pt') # Load a custom/local YOLOv5-medium model model = torch.hub.load('.', 'custom', 'yolov5m.pt', source='local') # Load from a local repository ``` For more information, visit https://pytorch.org/hub/ultralytics_yolov5. """ return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device selection. Args: pretrained (bool): Load pretrained weights into the model. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of model classes. Default is 80. autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model. Default is True. _verbose (bool): Print all information to screen. Default is True. device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', or a torch.device instance. Default is None. Returns: YOLOv5 model (torch.nn.Module): The YOLOv5-large model instantiated with specified configurations and possibly pretrained weights. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5l') ``` Notes: For additional details, refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 """ return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Perform object detection using the YOLOv5-xlarge model with options for pretraining, input channels, class count, autoshaping, verbosity, and device specification. Args: pretrained (bool): If True, loads pretrained weights into the model. Defaults to True. channels (int): Number of input channels for the model. Defaults to 3. classes (int): Number of model classes for object detection. Defaults to 80. autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper for handling different input formats. Defaults to True. _verbose (bool): If True, prints detailed information during model loading. Defaults to True. device (str | torch.device | None): Device specification for computing the model, e.g., 'cpu', 'cuda:0', torch.device('cuda'). Defaults to None. Returns: torch.nn.Module: The YOLOv5-xlarge model loaded with the specified parameters, optionally with pretrained weights and autoshaping applied. Example: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5x') ``` For additional details, refer to the official YOLOv5 PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 """ return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device) def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device. Args: pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True. channels (int, optional): Number of input channels. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model. Default is True. _verbose (bool, optional): If True, prints all information to screen. Default is True. device (str | torch.device | None, optional): Device to use for model parameters. Can be 'cpu', 'cuda', or None. Default is None. Returns: torch.nn.Module: YOLOv5-nano-P6 model loaded with the specified configurations. Example: ```python import torch model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda') ``` Notes: For more information on PyTorch Hub models, visit: https://pytorch.org/hub/ultralytics_yolov5 """ return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping, verbosity, and device selection. Args: pretrained (bool): If True, loads pretrained weights. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of object detection classes. Default is 80. autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model, allowing for varied input formats. Default is True. _verbose (bool): If True, prints detailed information during model loading. Default is True. device (str | torch.device | None): Device specification for model parameters (e.g., 'cpu', 'cuda', or torch.device). Default is None, which selects an available device automatically. Returns: torch.nn.Module: The YOLOv5-small-P6 model instance. Usage: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s6') model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s6') # load from a specific branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5s6.pt') # custom/local model model = torch.hub.load('.', 'custom', 'path/to/yolov5s6.pt', source='local') # local repo model ``` Notes: - For more information, refer to the PyTorch Hub models documentation at https://pytorch.org/hub/ultralytics_yolov5 Raises: Exception: If there is an error during model creation or loading, with a suggestion to visit the YOLOv5 tutorials for help. """ return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, and device. Args: pretrained (bool): If True, loads pretrained weights. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of model classes. Default is 80. autoshape (bool): Apply YOLOv5 .autoshape() wrapper to the model for file/URI/PIL/cv2/np inputs and NMS. Default is True. _verbose (bool): If True, prints detailed information to the screen. Default is True. device (str | torch.device | None): Device to use for model parameters. Default is None, which uses the best available device. Returns: torch.nn.Module: The YOLOv5-medium-P6 model. Refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 for additional details. Example: ```python import torch # Load YOLOv5-medium-P6 model model = torch.hub.load('ultralytics/yolov5', 'yolov5m6') ``` Notes: - The model can be loaded with pre-trained weights for better performance on specific tasks. - The autoshape feature simplifies input handling by allowing various popular data formats. """ return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping, verbosity, and device selection. Args: pretrained (bool, optional): If True, load pretrained weights into the model. Default is True. channels (int, optional): Number of input channels. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): If True, apply YOLOv5 .autoshape() wrapper to the model for input flexibility. Default is True. _verbose (bool, optional): If True, print all information to the screen. Default is True. device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda', or torch.device. If None, automatically selects the best available device. Default is None. Returns: torch.nn.Module: The instantiated YOLOv5-large-P6 model. Example: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # official model model = torch.hub.load('ultralytics/yolov5:master', 'yolov5l6') # from specific branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5l6.pt') # custom/local model model = torch.hub.load('.', 'custom', 'path/to/yolov5l6.pt', source='local') # local repository ``` Note: Refer to [PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5) for additional usage instructions. """ return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """ Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count, autoshaping, verbosity, and device selection. Args: pretrained (bool): If True, loads pretrained weights into the model. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of model classes. Default is 80. autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model. Default is True. _verbose (bool): If True, prints all information to the screen. Default is True. device (str | torch.device | None): Device to use for model parameters, can be a string, torch.device object, or None for default device selection. Default is None. Returns: torch.nn.Module: The instantiated YOLOv5-xlarge-P6 model. Example: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5x6') # load the YOLOv5-xlarge-P6 model ``` Note: For more information on YOLOv5 models, visit the official documentation: https://docs.ultralytics.com/yolov5 """ return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device) if __name__ == "__main__": import argparse from pathlib import Path import numpy as np from PIL import Image from utils.general import cv2, print_args # Argparser parser = argparse.ArgumentParser() parser.add_argument("--model", type=str, default="yolov5s", help="model name") opt = parser.parse_args() print_args(vars(opt)) # Model model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # model = custom(path='path/to/model.pt') # custom # Images imgs = [ "data/images/zidane.jpg", # filename Path("data/images/zidane.jpg"), # Path "https://ultralytics.com/images/zidane.jpg", # URI cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV Image.open("data/images/bus.jpg"), # PIL np.zeros((320, 640, 3)), ] # numpy # Inference results = model(imgs, size=320) # batched inference # Results results.print() results.save()