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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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()