AI Face Detection Data Tool - 30/03/2026 07:28 EDT
Budget: ₹2,500 – ₹0 INR
I have thousands of pictures scattered across several personal photo-album folders and I need an AI solution that can scan each image, detect every face it finds, and then give me useful data insights about those faces (counts per photo, frequency of specific people, basic age-group or emotion statistics if possible).
The core of the job is therefore twofold:
• build a reliable face-detection pipeline specialised for personal-album style images (no security-camera angles, mostly smartphone shots in varied lighting), and
• structure the results so I can analyse them later inside Python or export them to CSV for further data analysis.
Please use a mainstream deep-learning framework you are comfortable with—OpenCV, TensorFlow, PyTorch, or a comparable library is fine—as long as the final code runs on Windows and can be triggered from a simple command line. The model can be pre-trained (e.g., a RetinaFace or MTCNN backbone) provided you fine-tune or post-process it so false positives stay minimal.
Acceptance criteria
1. A script or notebook that takes an input folder path, processes every image, and outputs: image-level JSON/CSV with face count and, when possible, basic demographic tags.
2. Cropped face thumbnails stored in a parallel folder for quick visual checks.
3. README that explains environment setup and how to rerun the analysis on new folders.
That’s the whole scope—detect faces in my personal photos and hand me clean data I can explore further.
The core of the job is therefore twofold:
• build a reliable face-detection pipeline specialised for personal-album style images (no security-camera angles, mostly smartphone shots in varied lighting), and
• structure the results so I can analyse them later inside Python or export them to CSV for further data analysis.
Please use a mainstream deep-learning framework you are comfortable with—OpenCV, TensorFlow, PyTorch, or a comparable library is fine—as long as the final code runs on Windows and can be triggered from a simple command line. The model can be pre-trained (e.g., a RetinaFace or MTCNN backbone) provided you fine-tune or post-process it so false positives stay minimal.
Acceptance criteria
1. A script or notebook that takes an input folder path, processes every image, and outputs: image-level JSON/CSV with face count and, when possible, basic demographic tags.
2. Cropped face thumbnails stored in a parallel folder for quick visual checks.
3. README that explains environment setup and how to rerun the analysis on new folders.
That’s the whole scope—detect faces in my personal photos and hand me clean data I can explore further.