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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SenseBench

A benchmark for remote sensing low-level visual perception and description in large vision-language models.

🏠 github | 🤗 Hugging Face Subset

Overview

SenseBench is a remote sensing benchmark for evaluating low-level visual perception and description in large vision-language models.

Supported Tasks

Visual question answering
Text generation

Language

English

Data format

Each example contains image paths, a question, an answer, and metadata describing the distortion type.

{
  "id": "4fda312e-70d2-4df7-b1f7-2f06955bf338",
  "images": [
    "images/4fda312e-70d2-4df7-b1f7-2f06955bf338_0.png",
    "images/4fda312e-70d2-4df7-b1f7-2f06955bf338_1.png"
  ],
  "question": "Using the options provided, rate the overall quality of Image 2 compared to Image 1.\nA.No/Slight distortion\nB.Moderate distortion\nC.Severe distortion",
  "answer": "A",
  "meta": {
    "image_count": "multi",
    "modality": "RGB",
    "task": "how",
    "domain": "general",
    "distortion_family": "blur",
    "distortion_type": "blur_gaussian",
    "distortion_complexity": "single",
    "comparison": "intra-image"
  }
}

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