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Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.

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MNIST Double Digits Datasets Classification and Detectection

Datasets:

  • 64x64 size images containing two digits
  • digits range [0, 9]
  • digits size (bounding box size) 28x28


Algorithm used:

  • Classes: classification

    • two digits range from 0 to 9
    • model returned labels range from 0 to 19
    • reshape the labels into shape [N, 2]
    • each n data contain two class that the two digits belongs to
  • Bounding Boxes: Classification

    • two digits' position ranges from 0 to 37 ((64+1)-28)
    • model returned labels range from 0 to 148 (4*37)
    • reshape the labels in to shape [N, 2, 4]
    • each n data contain [x1, y1, x1+28, y1+28], [x2, y2, x2+28, y2+28] where x and y represents the axis and 1 and 2 represent first and second digits


Model:

     alt text


Result:

  • Validation datasets with model.pt.172:
    • Classification accuracy: 99.63
    • Detection IoU: 95.17
    • Valid time (GPU): ≈23.28
    • Valid time (CPU): ≈418