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 failed

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

CanolaTrack

CanolaTrack is a curated dataset for leaf-level multi-object tracking (MOT) and detection from top-down RGB imagery of Brassica napus (canola) plants. Each sequence records a single plant over time; frames contain annotated bounding boxes with persistent leaf IDs for tracking.

For baseline methods and a reference pipeline built on CanolaTrack, see LeafTrackNet (training, inference, and TrackEval integration) in our Github repo.

Dataset Summary

Domain: Plant phenotyping (leaf-level analysis, time series)
Modalities: RGB images (top-down)
Use cases: Multi-object tracking (leaf IDs), detection, re-identification
Content: Sequences of a single plant over days; each frame has MOT-style annotations
Annotations: gt/gt.txt per sequence with frame, leaf_id, x, y, w, h (pixels)
Extras: YOLOv10 proposals JSONs and LeafTrackNet model weightsfor reproducible tracking baselines

Repository Structure

CanolaTrack/ 
│  ├── train/
│  │   └── <plant_id>/
│  │         ├── gt/gt.txt # CSV: frame,id,x,y,w,h,,,*
│  │         └── img/{frame:08d}.jpg
│  └──val/
│     └── <plant_id>/
│            ├── gt/gt.txt
│            └── img/{frame:08d}.jpg
proposals/ # detection proposals for standardized benchmarking
│     ├── det_db_train.json
│     └── det_db_val.json
weights/ # detctors and tracker weights
      └── <files>

Supported Tasks and Benchmarks

Multi-Object Tracking (MOT) at the leaf level
Object Detection (per-frame leaf boxes)
Leaf Segmentation (per-frame leaf masks)

How to Cite

Please cite the dataset and the accompanying papers:

@article{leaftracknet2025,
  title={LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping},
  year={2025},
  author = {},
  url    = {}
}

CanolaTrack dataset© BASF SE 2025. This dataset may be freely used for non-commercial research and educational purposes.

Downloads last month: 78

Total file size:

1.75 GB