Action Segmentation Evaluation

Related tags

Deep Learning actseg
Overview

Reference Action Segmentation Evaluation Code

This repository contains the reference code for action segmentation evaluation.

If you have a bug-fix/improvement or if you want to add a new features please send a pull request or open an issue.

Example Usage

All the metrics have the same api.

from actseg.eval import MoFAccuracy, Edit

pred1 = [0, 0, 0, 1, 0, 1, 1, 1, 0]
pred2 = [1, 2, 3, 0, 0, 1, 2, 3, 0, 0, 0, 1, 2, 3, 0, 0, 0, 0]

target1 = [0, 0, 1, 1, 2, 1, 1, 0, 0]
target2 = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3]

metrics = [MoFAccuracy(), Edit()]
for p, t in zip([pred1, pred2], [target1, target2]):
    for m in metrics:
        m(targets=t, predictions=p)

for m in metrics:
    print(m)

# MoF: 0.3333333333333333
# Edit: 52.5

Metrics

Frame-wise Metrics

  1. MoF (Accuracy)
  2. F1Score
  3. IoD
  4. IoU

Segment-wise Metrics

  1. Edit (Edit distance or matching score)

Specifying Ignore Class

For some Metrics it is possible to specify the indices of classes to ignore (e.g. Background) by passing ignore_ids parameter to the constructor.

Authors

Yasser Souri: @yassersouri
Zijia Lu: @ZijiaLewisLu

Acknowledgement

Please see actseg/external for external sources used in this project.

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Comments
  • Add CI/CD

    Add CI/CD

    How should I do it?

    Links:

    https://medium.com/swlh/a-modern-set-up-for-python-package-development-f60b27a26bd7

    https://docs.github.com/en/actions/guides/building-and-testing-python

    opened by yassersouri 0
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