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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Dataset Structure

This dataset contains images categorized into different classes for medical image analysis. The dataset is organized as follows:

Data Split

The dataset is split into two main subsets: train and test.

Train Subset

The train subset contains images used for training machine learning models. It is further organized into subdirectories for each class.

CNV: Contains 37,205 images.
DME: Contains 11,348 images.
DRUSEN: Contains 8,616 images.
NORMAL: Contains 51,140 images.

Test Subset

The test subset contains images reserved for testing the trained models. It is organized in a similar manner to the train subset, with subdirectories for each class.

CNV: Contains 250 images.
DME: Contains 250 images.
DRUSEN: Contains 250 images.
NORMAL: Contains 250 images.

Usage

This dataset can be used for tasks such as classification, image recognition, and medical analysis. The provided class subdirectories indicate the different categories for the images.

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