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vhlab

Object Detection

vhlab Image Dataset

v6

2023-02-11 3:00am

Generated on Feb 10, 2023

Popular Download Formats

Pascal VOC XML
Common XML annotation format for local data munging (pioneered by ImageNet).
PaliGemma
PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
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Dataset Split

Train Set 86%
978Images
Valid Set 10%
117Images
Test Set 3%
39Images

Preprocessing

Auto-Orient: Applied
Isolate Objects: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Grayscale: Applied

Augmentations

Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 28% Maximum Zoom
Rotation: Between -28° and +28°
Shear: ±15° Horizontal, ±21° Vertical
Grayscale: Apply to 54% of images
Exposure: Between -44% and +44%
Blur: Up to 7px
Noise: Up to 11% of pixels
Cutout: 4 boxes with 32% size each
Mosaic: Applied
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Bounding Box: Shear: ±15° Horizontal, ±15° Vertical
Bounding Box: Exposure: Between -25% and +25%
Bounding Box: Blur: Up to 6px
Bounding Box: Noise: Up to 7% of pixels