I already have a functioning Python script that performs data analysis on image files, but its accuracy is not yet where I need it to be. I’m looking for someone who can dive into the current codebase, understand how it processes and evaluates the images, and then implement improvements that measurably boost the analysis accuracy. The core tasks will be: • Auditing the existing logic to spot weaknesses or outdated techniques • Introducing more reliable image‐processing methods—whether that means refining current OpenCV routines, adding scikit-image utilities, or integrating a lightweight CNN model with TensorFlow/PyTorch, I’m open to suggestions backed by results • Refactoring any parts that slow the script down or complicate maintenance while keeping the focus on accuracy • Supplying clear in-code comments and a brief changelog so I can trace what was modified and why Acceptance criteria: 1. The revised script processes the sample image set I’ll provide and demonstrates a clear accuracy improvement (I’ll share the baseline figures we need to beat). 2. No new external dependencies beyond standard Python imaging libraries unless we agree on them first. 3. Code runs on Python 3.9+ and passes the simple unit tests included in the repo. If tackling image data analysis and squeezing out better performance from existing Python scripts is familiar territory for you, let’s talk about the next steps.