TartanIMU-CoCEL_POSTECH

pash03023/TartanIMU-CoCEL_POSTECH

TartanIMU-CoCEL_POSTECH is a machine learning model from pash03023. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

Single unified model for IMU-only body-frame velocity estimation across four platforms (car / quadruped / drone / human). Leaderboard public 0.25392.

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Public

Updated Sep 20, 2026

Repo size

32 MB

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Public

Hugging Face

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.pt32 MB · 94%

At a glance

License: mit
Access: Public
Created: Sep 20, 2026
Updated: Sep 20, 2026
SHA: 88040d69

Model card

From the Hugging Face model README

TartanIMU Challenge — CoCEL@POSTECH Final Submission

Single unified model for IMU-only body-frame velocity estimation across four platforms (car / quadruped / drone / human). Leaderboard public 0.25392.

File	Description
`CoCEL_POSTECH.pt`	Frozen weights of the single unified model (7,979,267 params, one weight set)
`predict.py`	Self-contained inference entry point (no internet, no external repo imports)
`submission_CoCEL_POSTECH.csv`	The exact prediction file that produced the claimed leaderboard score
`requirements.txt`	Pinned dependencies (torch / numpy / pandas / scipy only)
`src/genz_imu/`	Model, encoders, batching, windowing, and the training script
`ref/TartanIMU/`	The organizers' metric module, unmodified — local scoring only

No normalisation constants file: the model is trained on the raw signal, and every preprocessing setting it needs is stored inside the checkpoint.

How to Inference

pip install -r requirements.txt
python predict.py --data /path/to/challenge_data \
                  --weights CoCEL_POSTECH.pt --out submission_CoCEL_POSTECH.csv

--data is the split root: the directory holding test/, index/ and sample_submission.csv. Output is 30,644 rows in 18 s on one RTX 5090.

⚠ CUDA is required to reproduce submission_CoCEL_POSTECH.csv exactly. requirements.txt pins the CUDA build of torch that produced it, so the pip install above fetches it; a CPU-only wheel computes the same function with different floating-point sums and does not match.

Notes

One model, one weight set. Every prediction for all four platforms comes from the single state_dict in CoCEL_POSTECH.pt. No ensemble, no weight averaging, no per-platform checkpoint.

Contact

Sanghyun Park: [email protected]
Seongjun Kim: [email protected]

License: mit
Created: Sep 20, 2026
Updated: Sep 20, 2026