View Source YoloFastNMS (YoloFastNMS v0.1.1)

Runs Non-Maximum Suppression (NMS) on a given Nx tensor (or directly a binary) using a Rust NIF.

Currently, this implementation is specifically designed for a tensor with the shape {84, 8400} or {1, 84, 8400}, which corresponds to the output of a YOLO model trained on the COCO dataset.

  • The tensor's 8400 rows represent the detected objects.
  • The first 4 columns contain the bounding box parameters: cx (center x), cy (center y), w (width), and h (height).
  • The remaining 80 columns represent the class probabilities.

Summary

Functions

run(tensor, prob_threshold, iou_threshold)

Runs Non-Maximum Suppression (NMS) on a tensor and returns a list of detected objects.

run_with_binary(tensor_binary, prob_threshold, iou_threshold)

Runs Non-Maximum Suppression (NMS) directly on a binary containing detection data.

Functions

run(tensor, prob_threshold, iou_threshold)

@spec run(Nx.Tensor.t(), prob_threshold :: float(), iou_threshold :: float()) :: [
  [float()]
]

Runs Non-Maximum Suppression (NMS) on a tensor and returns a list of detected objects.

Parameters

  • tensor: An Nx tensor with shape {84, 8400} containing detection data
  • prob_threshold: Minimum probability threshold (0..1) for detection confidence
  • iou_threshold: IoU threshold (0..1) for overlap detection

Returns a list of lists [cx, cy, w, h, prob, class_idx] where:

  • cx, cy: Center position coordinates of the detected object
  • w, h: Width and height of the bounding box
  • prob: Confidence score (between 0 and 1)
  • class_idx: Index of the detected class

run_with_binary(tensor_binary, prob_threshold, iou_threshold)

@spec run_with_binary(
  tensor_binary :: binary(),
  prob_threshold :: float(),
  iou_threshold :: float()
) ::
  [[float()]]

Runs Non-Maximum Suppression (NMS) directly on a binary containing detection data.

Parameters

  • tensor_binary: A binary containing detection data in shape {84, 8400}
  • prob_threshold: Minimum probability threshold (0..1) for detection confidence
  • iou_threshold: IoU threshold (0..1) for overlap detection

Returns a list of lists [cx, cy, w, h, prob, class_idx] where:

  • cx, cy: Center position coordinates of the detected object
  • w, h: Width and height of the bounding box
  • prob: Confidence score (between 0 and 1)
  • class_idx: Index of the detected class