box_classifer

Box Classifier

A bunch of CNNs (in parameter-sizes) trained to classify whether a checkbox is filled or not.

The training code, data generator can be found here: https://github.com/papaj2139/box_classifier

Models

Every model was trained on 10 epochs with 500 batches per epoch on 20000 images. Every image is 32x32.

model-5k.bin (5017 parameters) Architecture:

• Conv2D(1→8, 3×3) + ReLU + MaxPool(2)
• Conv2D(8→40, 3×3) + ReLU + MaxPool(2)
• GlobalAvgPool
• Dense(40→48) + ReLU + Dropout(0.2)
• Dense(48→1)

Loss trajectory:

Final test Acc: 99.72% Final train Acc: 99.57%

model-20k.bin (20373 parameters) Architecture:

• Conv2D(1→16, 3×3, pad=1), ReLU, MaxPool(2)
• Conv2D(16→100, 3×3, pad=1), ReLU, MaxPool(2)
• GlobalAvgPool2D
• Dense(100→56), ReLU, Dropout(0.2)
• Dense(56→1)

Loss trajectory:

Final test Acc: 99.85% Final train Acc: 99.68%

model-100k.bin (99713 parameters) Architecture:

• Conv2D(1→32, 3×3, pad=1), ReLU, MaxPool(2)
• Conv2D(32→128, 3×3, pad=1), ReLU, MaxPool(2)
• GlobalAvgPool2D
• Dense(128→480), ReLU, Dropout(0.3)
• Dense(480→1)

Loss trajectory:

Final test Acc: 99.65% Final train Acc: 99.81%

License

GPLv3