Reference for ultralytics/models/rtdetr/val.py
Note
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ultralytics.models.rtdetr.val.RTDETRDataset
RTDETRDataset(*args, data=None, **kwargs)
Bases: YOLODataset
Real-Time DEtection and TRacking (RT-DETR) dataset class extending the base YOLODataset class.
This specialized dataset class is designed for use with the RT-DETR object detection model and is optimized for real-time detection and tracking tasks.
Attributes:
Name | Type | Description |
---|---|---|
augment |
bool
|
Whether to apply data augmentation. |
rect |
bool
|
Whether to use rectangular training. |
use_segments |
bool
|
Whether to use segmentation masks. |
use_keypoints |
bool
|
Whether to use keypoint annotations. |
imgsz |
int
|
Target image size for training. |
Methods:
Name | Description |
---|---|
load_image |
Load one image from dataset index. |
build_transforms |
Build transformation pipeline for the dataset. |
Examples:
Initialize an RT-DETR dataset
>>> dataset = RTDETRDataset(img_path="path/to/images", imgsz=640)
>>> image, hw = dataset.load_image(0)
This constructor sets up a dataset specifically optimized for the RT-DETR (Real-Time DEtection and TRacking) model, building upon the base YOLODataset functionality.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args
|
Any
|
Variable length argument list passed to the parent YOLODataset class. |
()
|
data
|
dict | None
|
Dictionary containing dataset information. If None, default values will be used. |
None
|
**kwargs
|
Any
|
Additional keyword arguments passed to the parent YOLODataset class. |
{}
|
Source code in ultralytics/models/rtdetr/val.py
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build_transforms
build_transforms(hyp=None)
Build transformation pipeline for the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
hyp
|
dict
|
Hyperparameters for transformations. |
None
|
Returns:
Type | Description |
---|---|
Compose
|
Composition of transformation functions. |
Source code in ultralytics/models/rtdetr/val.py
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load_image
load_image(i, rect_mode=False)
Load one image from dataset index 'i'.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
i
|
int
|
Index of the image to load. |
required |
rect_mode
|
bool
|
Whether to use rectangular mode for batch inference. |
False
|
Returns:
Name | Type | Description |
---|---|---|
im |
Tensor
|
The loaded image. |
resized_hw |
tuple
|
Height and width of the resized image with shape (2,). |
Examples:
Load an image from the dataset
>>> dataset = RTDETRDataset(img_path="path/to/images")
>>> image, hw = dataset.load_image(0)
Source code in ultralytics/models/rtdetr/val.py
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ultralytics.models.rtdetr.val.RTDETRValidator
RTDETRValidator(dataloader=None, save_dir=None, args=None, _callbacks=None)
Bases: DetectionValidator
RTDETRValidator extends the DetectionValidator class to provide validation capabilities specifically tailored for the RT-DETR (Real-Time DETR) object detection model.
The class allows building of an RTDETR-specific dataset for validation, applies Non-maximum suppression for post-processing, and updates evaluation metrics accordingly.
Attributes:
Name | Type | Description |
---|---|---|
args |
Namespace
|
Configuration arguments for validation. |
data |
dict
|
Dataset configuration dictionary. |
Methods:
Name | Description |
---|---|
build_dataset |
Build an RTDETR Dataset for validation. |
postprocess |
Apply Non-maximum suppression to prediction outputs. |
Examples:
Initialize and run RT-DETR validation
>>> from ultralytics.models.rtdetr import RTDETRValidator
>>> args = dict(model="rtdetr-l.pt", data="coco8.yaml")
>>> validator = RTDETRValidator(args=args)
>>> validator()
Notes
For further details on the attributes and methods, refer to the parent DetectionValidator class.
Source code in ultralytics/models/yolo/detect/val.py
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build_dataset
build_dataset(img_path, mode='val', batch=None)
Build an RTDETR Dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
img_path
|
str
|
Path to the folder containing images. |
required |
mode
|
str
|
|
'val'
|
batch
|
int
|
Size of batches, this is for |
None
|
Returns:
Type | Description |
---|---|
RTDETRDataset
|
Dataset configured for RT-DETR validation. |
Source code in ultralytics/models/rtdetr/val.py
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postprocess
postprocess(
preds: Union[Tensor, List[Tensor], Tuple[Tensor]],
) -> List[Dict[str, torch.Tensor]]
Apply Non-maximum suppression to prediction outputs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
preds
|
Tensor | List | Tuple
|
Raw predictions from the model. If tensor, should have shape (batch_size, num_predictions, num_classes + 4) where last dimension contains bbox coords and class scores. |
required |
Returns:
Type | Description |
---|---|
List[Dict[str, Tensor]]
|
List of dictionaries for each image, each containing: - 'bboxes': Tensor of shape (N, 4) with bounding box coordinates - 'conf': Tensor of shape (N,) with confidence scores - 'cls': Tensor of shape (N,) with class indices |
Source code in ultralytics/models/rtdetr/val.py
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pred_to_json
pred_to_json(predn: Dict[str, Tensor], pbatch: Dict[str, Any]) -> None
Serialize YOLO predictions to COCO json format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
predn
|
Dict[str, Tensor]
|
Predictions dictionary containing 'bboxes', 'conf', and 'cls' keys with bounding box coordinates, confidence scores, and class predictions. |
required |
pbatch
|
Dict[str, Any]
|
Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'. |
required |
Source code in ultralytics/models/rtdetr/val.py
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