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The JWT signature verification failed. Check the signing key and the algorithm.
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.
Zoo Animal Re-Identification Dataset
A dataset for animal re-identification with 2,705 body images, 1,192 face crops, and 6 configurations.
Configurations
1. face_and_body
Individual frames with both face and body crops.
Features:
date: Date of capture (YYYY-MM-DD)time: Time of capture (HH:MM:SS)class: Animal namevideo: Source video filenameframe_number: Frame number in videocamera: Camera IDface_image: Cropped face imagebody_image: Cropped body/full image
2. body_only
Individual frames with body crops only.
Features:
date,time,class,video,frame_number,camera: Same as abovebody_image: Cropped body image
3. original_with_face_body_bbox
Full original frames.
Features:
date,time,class,video,frame_number,camera: Same as aboveimage: Original full image
4. original_with_body_bbox
Full original frames.
Features:
date,time,class,video,frame_number,camera: Same as aboveimage: Original full image
5. face_tracklets
Face images grouped by tracklet (animal + video). Each row contains all face crops from one animal in one video, ordered by frame number.
Features:
class: Animal namevideo: Source video filenamecamera: Camera IDframe_numbers: List of frame numbers (ordered)face_images: Sequence of face images (ordered by frame)
6. body_tracklets
Body images grouped by tracklet (animal + video). Each row contains all body crops from one animal in one video, ordered by frame number.
Features:
class: Animal namevideo: Source video filenamecamera: Camera IDframe_numbers: List of frame numbers (ordered)body_images: Sequence of body images (ordered by frame)
Usage
from datasets import load_dataset
# Load individual frames with face and body
ds = load_dataset("Maxscha/test", "face_and_body", split="test")
print(ds[0]["face_image"]) # PIL Image
print(ds[0]["class"]) # Animal name
# Load face tracklets
ds = load_dataset("Maxscha/test", "face_tracklets", split="test")
print(len(ds[0]["face_images"])) # Number of faces in this tracklet
print(ds[0]["class"]) # Animal name for this tracklet
# Load body tracklets
ds = load_dataset("Maxscha/test", "body_tracklets", split="test")
for img in ds[0]["body_images"]:
print(img) # Each PIL Image in the tracklet sequence
Animals
The dataset contains images of 5 animals:
- Sango
- Tilla
- M'Penzi
- Bibi
- Djambala
License
MIT
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