Summary
A user is seeking methods to detect mirrored selfie images before processing them with Vision-Language Models (VLMs) and face embedding extractors. The challenge arises because VLMs like Qwen and Florence, trained on flipped data, are often insensitive to backwards text. The proposed solution involves using OCR (e.g., EasyOCR) on text crops and comparing read scores between normal and flipped versions to identify mirrored images.
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