Enhance Stripe Recognition Code
Budget: ₹37,500 – ₹75,000 INR
I already have a working Python script that identifies stripe-like patterns in still images, but it needs to move from “proof-of-concept” to a polished, deployable module. The current model does a reasonable job on simple samples, yet its accuracy drops with noisy backgrounds, it only understands a handful of stripe geometries, and it processes large batches slower than I’d like.
The brief is straightforward:
• Improve accuracy: fine-tune the existing algorithm—or replace it—so it handles challenging lighting and mixed-texture scenes without a spike in false positives.
• Add more pattern types: extend recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores.
• Optimize performance: refactor the pipeline for faster image loading, GPU-aware inference, and leaner memory use so it remains responsive on large datasets.
Everything runs in Python, so please stay within that ecosystem. You are free to introduce OpenCV, scikit-image, PyTorch, TensorFlow, or other libraries, provided the final solution installs cleanly with a requirements.txt and runs from a single entry-point script or Jupyter notebook.
Input will be folders of images; no video or live feed integration is required at this stage, but laying groundwork for future expansion is a plus. I will supply a labeled image set for benchmarking and expect a short report showing accuracy improvements and speed comparisons versus the current baseline.
Deliverables
• Refactored Python code with the three enhancements fully integrated
• Requirements file and concise setup instructions
• Demonstration notebook or CLI script that reproduces the results on my test set
• Brief performance/accuracy report and recommendations for next steps
Acceptance Criteria
1. At least X % accuracy lift on the provided validation set (we’ll define X together once you review the baseline).
2. Runtime per image reduced by a meaningful margin without sacrificing accuracy.
3. Clean install and run on a fresh environment (Windows or Linux).
Once these markers are met and verified on my side, the project is complete.
The brief is straightforward:
• Improve accuracy: fine-tune the existing algorithm—or replace it—so it handles challenging lighting and mixed-texture scenes without a spike in false positives.
• Add more pattern types: extend recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores.
• Optimize performance: refactor the pipeline for faster image loading, GPU-aware inference, and leaner memory use so it remains responsive on large datasets.
Everything runs in Python, so please stay within that ecosystem. You are free to introduce OpenCV, scikit-image, PyTorch, TensorFlow, or other libraries, provided the final solution installs cleanly with a requirements.txt and runs from a single entry-point script or Jupyter notebook.
Input will be folders of images; no video or live feed integration is required at this stage, but laying groundwork for future expansion is a plus. I will supply a labeled image set for benchmarking and expect a short report showing accuracy improvements and speed comparisons versus the current baseline.
Deliverables
• Refactored Python code with the three enhancements fully integrated
• Requirements file and concise setup instructions
• Demonstration notebook or CLI script that reproduces the results on my test set
• Brief performance/accuracy report and recommendations for next steps
Acceptance Criteria
1. At least X % accuracy lift on the provided validation set (we’ll define X together once you review the baseline).
2. Runtime per image reduced by a meaningful margin without sacrificing accuracy.
3. Clean install and run on a fresh environment (Windows or Linux).
Once these markers are met and verified on my side, the project is complete.
Related categories:
PHP
JavaScript
Python
Software Architecture
Machine Learning (ML)
Image Processing
OpenCV
Deep Learning