• Python3
• OpenCV
• Jupyter Notebook
• Numpy
• Scikit learn
• matplotlib
• Tensorflow
• Keras
• AWS EC2 with bitfusion
• Nvidia CUDA
• Google Compute Engine
• Julia + Knet.jl

Achieve best possible recognition of dots on each dice wall. This neural model will be used in camera, now presented here in a notebook. Unfortunately, real-time update of matplotlib plot is rather anoying, now this feature is moved there to a python script. This works in separate, new window with real-time update.

• First 120 real images, 20 images per one side of dice. This is multiplicated to 60480 images due to warping perspectives and rotations in openCV-s.

Code for this is available here This is not this kind-of beautiful code, but it's useful for me and I hope I'll have some time later to refactor it

• Next-tier dataset is 600 images with 100 for each side of a dice with 15 different color settings for dots, dices and background. This is transformed to 100800 dices with 64x64x3 or 64x64x1 sized, depends on particular network model.

• Last dataset is 984 images, 164 per wall. This time, dices are not focused in the center of a background which lets me now use trainings with rectangular images with aspect ratio 4:3, the same as photos taken from camera. Now this set is available in 640x480x1, 320x240x3, 320x240x1, 160x120x1 and 106x79x1

Last pre-trained model are using the smallest version 106x79x1, due to horrendous computation time, event on AWS Nvidia K80 TESLA GPU

• Trained models with accuracy around 99% for 64x64x1 (grayscale) images with set colors of both background and dice
• Models with accuracy over 98% for 64x64x1 (grayscale) and 64x64x3 (RGB) images with 15 different sets of colors.
• First model with accuracy of 68.03% for 106x79x1 (grayscale) finally breaking the 64x64 initial size od learned images

All neural network models are trained on AWS EC2 and Google Compute Engine (so far mainly AWS) On AWS it's using p2.xlarge instance with Nvidia TESLA K80 GPU

• Different numbers of parameters in compiled models for both Sequential and Functional API model
• Troubles with setting proper batch_size according to each images resolutions (number of parameters to store in GPU memory)
• Almost not-learnable images with recognition greater than 64x64. The only success so far is with 106x79 images (68.03% accuracy). With 160x120 the error/accuracy rate remains unchanged (marginal upgrades, can be treated as noise)

There is actually the major idea that comes through from articles and scientific works which presents very good performance with neural model learned with higher number of epochs. I decided to train my network for usually 20 epochs as this is very time/money consuming. However the truth is the models should be learned about 100 epochs at least. In the examples where performance is good and accuracy is above 90% it's not so important but with examples as the last one, with 106x79 images it's worth trying.

• The first attempt, with not 100 epochs but as for now - continuing training of a model, prelearned in 20 epochs, for the next 20 epochs (40 combined), and then apply 20 epochs again which makes all together 60 epochs The outcome with plots presenting accuracy and error rate are available here As a comparison there is the notebook the same model during first 20 epochs.

• Accuracy after 20 epochs: 68.03%

• Accuracy after 40 epochs: 78.02%

• Accuracy after 60 epochs: 81.59%

As for now, almost all notebooks are based on Keras framework with Tensorflow backend, due to simplicity at the beginning.

The future will focus on changing tech-stack towards Tensorflow. More ambitious plans is to leave TF ans Keras and create CNN from scratch, purely in C++ with further application of Nvidia CUDA support for speed.

This is created via this model