cnic vs utility bills detection Computer Vision Model
4392 Total Images
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Dataset Split
Train Set 82%
3622Images
Valid Set 12%
512Images
Test Set 6%
258Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 224x224
Augmentations
Outputs per training example: 2
Flip: Horizontal
90° Rotate: Applied
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Rotation: Between -15° and +15°
Shear: ±10° Horizontal, ±10° Vertical
Grayscale: Apply to 15% of images
Hue: Between -15° and +15°
Saturation: Between -25% and +25%
Brightness: Between -15% and +15%
Exposure: Between -10% and +10%
Blur: Up to 3px
Noise: Up to 1% of pixels
Motion Blur: Length 100px, Angle: 0°, Frames: 1
Camera Gain: Variance: 0.05