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_classifierModels
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:
loss-5kFinal 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:
lossFinal 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:
lossFinal test Acc: 99.65%
Final train Acc: 99.81%
License
GPLv3
