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https://github.com/fpt-thaituan/detect-yoga-poses-and-correction-in-real-time-using-machine-learning-algorithms
Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.
https://github.com/fpt-thaituan/detect-yoga-poses-and-correction-in-real-time-using-machine-learning-algorithms
blazepose computer-vision deep-learning long-short-term-memory lsmt machine-learning pose-estimation support-vector-machines svc-model yoga-classfication
Last synced: 21 days ago
JSON representation
Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.
- Host: GitHub
- URL: https://github.com/fpt-thaituan/detect-yoga-poses-and-correction-in-real-time-using-machine-learning-algorithms
- Owner: FPT-ThaiTuan
- Created: 2023-12-21T15:49:04.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-18T14:56:11.000Z (7 months ago)
- Last Synced: 2024-05-18T15:48:17.267Z (7 months ago)
- Topics: blazepose, computer-vision, deep-learning, long-short-term-memory, lsmt, machine-learning, pose-estimation, support-vector-machines, svc-model, yoga-classfication
- Language: Python
- Homepage:
- Size: 221 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md