Datasets consist of 18 columns. The first one determines the hand configuration. The rest correspond to the data gloves sensors input. Each hand configuration is measured 10 times.
- review architecture (add multiple filters)
- Visualise w/ tf.tensorboard
- create inferring script
- transfer model building function to utils.py
- k-fold cross validation test
- create a model with an intermediate numper of parameters to 25k - 275k
- rounding script for datasets
- name columns appropriately
- implement exploration/visualization scripts
- batch evaluation script
- use argparse to get inputs in all scripts
- modeling name scheme (model name, val loss, epoch)
- change script to work with directories or files
- Prompt the user to calibrate and then turn off auto-calibration
- Insert new dataset
- Batch normalization
- Test L2 regularization
- Check decision trees and gradient boosting
- Add evaluation in the end of the training script
- train, train-val, val, test
- Try L1 regulization (though the sparsity of the features is not certain)
- decide stopping strategy (try early stopping)
- explore how linearly separable is our data and try SVMs
- consider separating the knuckle inputs with the finger inputs
- integrate in Virtual Sign
- Check unsupervised pre-training
- hyper-parameter search script (check hyperas)
- upload graph of the models
- add the SVM classifier to Virtual Sign
- use the new model of gloves: add pitch, roll and yaw inputs, adapt calibration process, output 57 instead of 42 classes
- capture new dataset