In this project, I implemented a deep learning architecture Mobilenet to identification and classification batik to 20 classes (batik-bali, batik-betawi, batik-celup, batik-cendrawasih, batik-ceplok, batik-ciamis, batik-garutan, batik-gentongan, batik-kawung, batik-keraton, batik-lasem, batik-megamendung, batik-parang, batik-pekalongan, batik-priangan, batik-sekar, batik-sidoluhur, batik-sidomukti, batik-sogan, batik-tambal)
For doing this project we used the following resources: https://www.kaggle.com/dionisiusdh/indonesian-batik-motifs
The architecture of this project is based on Mobilnet.
we use a pre-trained model Because the pre-trained models have their parameters more adjusted and the filters they have learned are more polished, in consequence, we can avoid the process of teaching the network those filters and the only thing that we should focus on is to adjust the other parameters to the specifications of our data set.
#How to use
!pip install tflite-model-maker (for get all requirment) and you'll get:
- keras-preprocessing=1.1.2
- tensorboard=2.5
- numpy==1.16.1
- opencv-python==4.0.0.21
- scikit-learn=0.19.0
- python=3.7
- data_training
- data_testing
- validation
- PREDICT LABEL
