The dataset viewer is not available for this dataset.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RepCount-A — playable MP4 package
RepCount Part-A (LLSP) repackaged as plain H.264 MP4 files plus per-split JSON annotations, for direct use in video-LLM evaluation pipelines and standard players.
- 1,041 videos — train 758 / validation 131 / test 152 (~9 GB, mostly 360p, native aspect)
- Fine-grained annotations: repetition
count+ per-cycle boundary frame indices (cycle_bounds)
Structure
train/ 758 .mp4
validation/ 131 .mp4
test/ 152 .mp4
train_annotations.json
validation_annotations.json
test_annotations.json
Each annotation entry:
{
"video": "stu10_0.mp4",
"video_id": "stu10_0",
"source_name": "stu10_0.mp4",
"action_type": "pull_up",
"count": 14,
"cycle_bounds": [1, 23, 23, 40, ...]
}
cycle_bounds is a flat list of frame indices marking the start/end of each action
cycle (pairs), as annotated by the original authors. Standard metrics: MAE and
OBO (off-by-one accuracy).
Provenance
Videos and annotations originate from the official RepCount release
(RepCountA.tar.gz, LLSP structure), obtained via the
lmms-lab-eval/repcounta-lance
mirror; video bytes are extracted as-is (no re-encoding). Annotation frame indices
were spot-verified against decoded frame counts.
License / attribution
RepCount is released for academic use by its authors. If you use this data, cite:
@inproceedings{hu2022transrac,
title={TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting},
author={Hu, Huazhang and Dong, Sixun and Zhao, Yiqun and Lian, Dongze and Li, Zhengxin and Gao, Shenghua},
booktitle={CVPR},
year={2022}
}
Official dataset page: https://svip-lab.github.io/dataset/RepCount_dataset.html
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