yolov8-obb angle about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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from ultralytics.

@1005183361 hi there! 👋

For obtaining orientation angles from YOLOv8-obb (oriented bounding boxes) within the 0-360 degree range, you can ensure that the angle output from the model is properly normalized and adjusted. If the output doesn't directly reflect this range, you might need to convert or adjust the predictions accordingly.

In the case that your model outputs angle predictions in radians or another scale, you can convert these to degrees using a simple calculation in Python:

import numpy as np

# Example angle in radians
angle_rad = model_output_angle  # This would be your model's angle output in radians
angle_deg = np.degrees(angle_rad) % 360  # Converts to degrees and normalizes to 0-360

Ensure that you apply this conversion after you receive the angle output from your model predictions. If further tweaks are necessary, it could involve modifications in the post-processing step where the model's raw output is interpreted.

I hope this helps! Let me know if you need more details. 😊

from ultralytics.

I used the obb model to predict the rotation of the screw locking line (the labeling method is that the positive direction of the x-axis is 0, and the clockwise direction is the increasing direction, until a circle of 360 degrees). I modified the
angle = ( in head.py angle.sigmoid()) * 2*math.pi,
it is found that it does not work. When the rotation angle exceeds 180 degrees, the model will predict incorrectly. What other codes should I modify?

from ultralytics.

Hi there! 👋

It sounds like you're encountering issues with angle normalization in the YOLOv8-obb model. The modification you made in head.py is a good start, but it seems like the model might still be interpreting angles in a range that doesn't fully accommodate your 0-360 degrees requirement.

One approach could be to adjust the angle post-prediction to ensure it fits within the 0-360 range. Here’s a quick Python snippet that might help:

import math

# Assuming 'angle' is your model's output in radians
angle_deg = (angle.sigmoid().item() * 360) % 360  # Normalize to 0-360 degrees

This modification ensures that the output angle is always within the desired range by converting the sigmoid output directly to degrees and then applying modulo 360.

If the issue persists, it might be necessary to delve deeper into how the angles are being trained and whether the loss function correctly penalizes deviations for angles near the 0/360-degree boundary.

Hope this helps! Let me know if there's anything else you'd like to explore. 😊

from ultralytics.