Based on the provided hint and the content of the Python script, here are the identified potential issues:

1. **Incorrect Label Range**:
    - **Evidence**: `'_LABELS = [str(x) for x in range(0, 360, 5)]'`
    - **Description**: The script incorrectly generates labels from 0 to 355 in steps of 5, which seems to represent the angles at which the images were taken. However, the dataset description states that it contains 7200 color images of 100 objects with 72 images per object, suggesting that each object should have its own label. Therefore, generating labels based on angles might be a misinterpretation of how labels should be assigned, especially considering that labels are usually intended to represent distinct categories or classes in a dataset.

2. **Mismatch Between Extracted Label and Defined Labels**:
    - **Evidence**: 
        ```python
        label = file_name.split("_")[2].split(".")[0]
        ```
    - **Description**: The extraction mechanism for labels from file names uses the third part split by an underscore and the first part split by a dot. Given the defined labels are angles (0 to 355), if the file naming convention doesn't strictly adhere to encoding the angle in the anticipated segment of the file name, it will result in incorrect label extraction. This strategy assumes a very specific naming convention without validation, raising the risk of mismatched or incorrect labels if file names do not follow expected patterns rigorously.

Both issues highlight potential discrepancies in label handling (generation and extraction), which can significantly impact the dataset's usability for training machine learning models, where accurate labels are crucial.