# YOLO This package contains multiple helpers and methods for the YOLO Objet detection task: ## YoloLoss The YoloLoss is an implementation of the [YoloLoss](https://github.com/WongKinYiu/yolov7/blob/main/utils/loss.py) used by the Yolov7 repository. We expose an interface to the loss that could be configured using the following properties: ```python from code_loader.helpers.detection.yolo.loss import YoloLoss LOSS_FN = YoloLoss(num_classes: int, default_boxes: List[NDArray[np.int32]], overlap_thresh: float, background_label: int, features: List[Tuple[int, int]] = [], anchors: Optional[NDArray[np.int32]] = None, from_logits: bool = True, weights: List[float] = [4.0, 1.0, 0.4], max_match_per_gt: int = 10, image_size: Union[Tuple[int, int], int] = (640, 640), cls_w: float = 0.3, obj_w: float = 0.7, box_w: float = 0.05, yolo_match: bool = False): ```

Args
num_classes	The number of classes in the dataset
default_boxes	A List of NDArray representing the model's grid . see Grid.generate_anchors()
overlap_thresh	The Matcher overlap threshold that sets what constitutes a match. YOLO default is 0.0625
background_label	If no background_label was used during training should be set to NUM_CLASSES+1
features	Only required if yolo_match is True. The size of the predictions heads your model uses [[H1,W1],[H2,W2]...]
anchors	Only required if yolo_match is True. The anchors used in your model
from_logits	True if the model was exported without a sigmoid. False if the model was exported as recommended by us
weights	the weights used to scale the object loss
max_match_per_gt	The number of priors matches per GT. Yolov7 default is 10
Image_size	the size of your images.
cls_w	Classification loss weight
obj_w	The object loss weight
box_w	The regression loss weight
yolo_match	When yolo_match is True we use the same Matcher as YoloV7. When yolo_match is False we use a slightly faster matcher, that approximates the YoloV7 matcher.

This loss has a `__call__` method that computes the yolo\_loss: ```python iou_loss, obj_loss, class_loss = LOSS_FN(y_true: tf.Tensor, y_pred: Tuple[List[tf.Tensor], List[tf.Tensor]]) ```

Args
y_true	The ground truth encoded into a shape of [MAX_BB,5]. the 5 channels represent [X,Y,W,H,class]
y_pred	A tuple (loc,class) composed of: - loc. A list the size of the number of heads. Each element is of size [Batch,#BB,4]. The channels represent [X,Y,W,H] - class. A list the size of the number of heads. Each element is of size [Batch,#BB,#classes+1]

This returns the three losses of the YOLO repo (IOU loss, Object loss, Classification loss) ## Decoder Since we recommend exporting the model without the `NMS` and `top-k` components we need a Decoder model that can serve as a head to filter only the most confident bounding box. We expose an interface for our default decoder: ```python from code_loader.helpers.detection.yolo.decoder import Decoder DECODER = Decoder(self, num_classes: int, background_label: int, top_k: int, conf_thresh: float, nms_thresh: float, max_bb_per_layer: int = 20, max_bb_per_layer: int = 20, max_bb: int = 20) ```

Args
num_classes	The number of classes in the dataset
background_label	If no background_label was used during training should be set to NUM_CLASSES+1
top_k	The number of BB for the top_k param. Per-layer and per-class.
conf_thresh	A threshold for the confidence. BB with confidence lower with this will not be shown by the decoder
nms_thresh	The NMS threshold for IOU-overlap calculation. see Tensorflow's non_max_suppression IOU supression param for more details.
max_bb_per_layer	The maximum amount of BB selected per layer
max_bb	The maximum amount of BB selected overall

This decoder has a `__call__` function that returns a list of the selected bounding\_boxes

bounding_boxes = DECODER(loc_data: List[tf.Tensor], conf_data: List[tf.Tensor],
        prior_data: List[NDArray[np.float32]],
         from_logits: bool = True, decoded: bool = False)

Args
loc_data	A list the size of the number of heads. Each element is of size [Batch,#BB,4]. The channels represent [X,Y,W,H]
conf_data	A list the size of the number of heads. Each element is of size [Batch,#BB,#classes+1]
prior_data	a List of NDArray representing the model's grid . see Grid.generate_anchors()
from_logits	True if the model was exported without a sigmoid. False if the model was exported as recommended by us
decoded	True if the model was exported with a decoder, as recommended by us. False otherwise (i.e. the predictions are still relative to anchors and are not in image coordinates)

## Grid This class represents the YOLO priors grid.

from code_loader.helpers.detection.yolo.grid import Grid

BOXES_GENERATOR = Grid(image_size: Tuple[int, int], feature_maps: Tuple[Tuple[int, int], ...],
                       box_sizes: Tuple[Tuple[float, ...], ...], strides: Tuple[int, ...],
                       offset: int)

Args
image_size	the image size we use for inference
feature_maps	the shapes of the model heads ((H1,W1),(H2,W2))..
box_sizes	The shape of the anchors as set in the Yolov7 YAML
strides	The strides that connects the head_size to image_size (IMAGE_SIZE/HEAD_SIZE)
offset	0 if the grid starts from (0,0) as expected by the YOLO repo

This class has a `generate_anchors()` method that creates the grid used by the loss and decoder.
```python DEFAULT_BOXES = BOXES_GENERATOR.generate_anchors() ``` DEFAULT\_BOXES is of type List\[NDArray\[np.float32]]. each entry is an entry sized (#head-BB,4) representing the coordinates for the bounding box located in each head. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.tensorleap.ai/tensorleap-integration/python-api/helpers/detection/yolo.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.