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

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Nameplate Detection Dataset

This dataset contains 1000 images for object detection of nameplates and related warning signs.

Dataset Details

Total Images: 1000
Classes: 4 (nameplate, w, warning, yellow)
Train: 702 images
Validation: 149 images
Test: 149 images

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("kahua-ml/nameplate1")

# Access splits
train_data = dataset["train"]
val_data = dataset["valid"] 
test_data = dataset["test"]

# Example usage
example = train_data[0]
print(f"Image ID: {example['image_id']}")
print(f"Image size: {example['width']}x{example['height']}")
print(f"Number of objects: {len(example['objects']['bbox'])}")
print(f"Categories: {example['objects']['category']}")

Data Format

Each example contains:

image_id: Unique identifier for the image
image: PIL Image object
width: Image width in pixels
height: Image height in pixels
objects: Dictionary containing:
- bbox: List of bounding boxes [x, y, width, height] in COCO format
- category_id: List of category IDs
- category: List of category names
- area: List of bounding box areas
- iscrowd: List of crowd flags

Original Dataset

This dataset was sourced from Roboflow Universe: https://universe.roboflow.com/flying-squirrels/nameplate-detection

Citation

@misc{
    nameplate-detection_dataset,
    title = { Nameplate Detection Dataset },
    type = { Open Source Dataset },
    author = { Flying Squirrels },
    howpublished = { \url{ https://universe.roboflow.com/flying-squirrels/nameplate-detection } },
    url = { https://universe.roboflow.com/flying-squirrels/nameplate-detection },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { mar },
    note = { visited on 2025-01-25 },
}

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