Bases: Model
YOLO Modelo NAS para la detección de objetos.
Esta clase proporciona una interfaz para los modelos YOLO-NAS y extiende la clase Model
del motor Ultralytics .
Está diseñada para facilitar la tarea de detección de objetos utilizando modelos YOLO-NAS preentrenados o entrenados a medida.
Ejemplo
from ultralytics import NAS
model = NAS('yolo_nas_s')
results = model.predict('ultralytics/assets/bus.jpg')
Atributos:
Nombre |
Tipo |
Descripción |
model |
str
|
Ruta al modelo preentrenado o nombre del modelo. Por defecto es 'yolo_nas_s.pt'.
|
Nota
YOLO-Los modelos NAS sólo admiten modelos preentrenados. No proporciones archivos de configuración YAML.
Código fuente en ultralytics/models/nas/model.py
| class NAS(Model):
"""
YOLO NAS model for object detection.
This class provides an interface for the YOLO-NAS models and extends the `Model` class from Ultralytics engine.
It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models.
Example:
```python
from ultralytics import NAS
model = NAS('yolo_nas_s')
results = model.predict('ultralytics/assets/bus.jpg')
```
Attributes:
model (str): Path to the pre-trained model or model name. Defaults to 'yolo_nas_s.pt'.
Note:
YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files.
"""
def __init__(self, model="yolo_nas_s.pt") -> None:
"""Initializes the NAS model with the provided or default 'yolo_nas_s.pt' model."""
assert Path(model).suffix not in {".yaml", ".yml"}, "YOLO-NAS models only support pre-trained models."
super().__init__(model, task="detect")
@smart_inference_mode()
def _load(self, weights: str, task: str):
"""Loads an existing NAS model weights or creates a new NAS model with pretrained weights if not provided."""
import super_gradients
suffix = Path(weights).suffix
if suffix == ".pt":
self.model = torch.load(weights)
elif suffix == "":
self.model = super_gradients.training.models.get(weights, pretrained_weights="coco")
# Standardize model
self.model.fuse = lambda verbose=True: self.model
self.model.stride = torch.tensor([32])
self.model.names = dict(enumerate(self.model._class_names))
self.model.is_fused = lambda: False # for info()
self.model.yaml = {} # for info()
self.model.pt_path = weights # for export()
self.model.task = "detect" # for export()
def info(self, detailed=False, verbose=True):
"""
Logs model info.
Args:
detailed (bool): Show detailed information about model.
verbose (bool): Controls verbosity.
"""
return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
@property
def task_map(self):
"""Returns a dictionary mapping tasks to respective predictor and validator classes."""
return {"detect": {"predictor": NASPredictor, "validator": NASValidator}}
|
task_map
property
Devuelve un diccionario que asigna las tareas a las respectivas clases de predictor y validador.
__init__(model='yolo_nas_s.pt')
Inicializa el modelo NAS con el modelo proporcionado o por defecto 'yolo_nas_s.pt'.
Código fuente en ultralytics/models/nas/model.py
| def __init__(self, model="yolo_nas_s.pt") -> None:
"""Initializes the NAS model with the provided or default 'yolo_nas_s.pt' model."""
assert Path(model).suffix not in {".yaml", ".yml"}, "YOLO-NAS models only support pre-trained models."
super().__init__(model, task="detect")
|
info(detailed=False, verbose=True)
Registra la información del modelo.
Parámetros:
Nombre |
Tipo |
Descripción |
Por defecto |
detailed |
bool
|
Mostrar información detallada sobre el modelo.
|
False
|
verbose |
bool
|
|
True
|
Código fuente en ultralytics/models/nas/model.py
| def info(self, detailed=False, verbose=True):
"""
Logs model info.
Args:
detailed (bool): Show detailed information about model.
verbose (bool): Controls verbosity.
"""
return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
|