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- 3.1. Model Architecture
- 3.2. Samples
- 3.3. Accuracy
This repository contains code and results for detecting vowels and consonants in Tamil language characters.
- Install pipenv using the command
pip insalll pipenv
- Install the project dependencies by running the command
pipenv install
in the project directory which will set up the dependencies after creating a virtual env.
You can find the jupyter notebook for this project in notebooks/Vowel_consonant_classification.ipynb
. Running it should be fairly simple as the code is well-commented and explained fairly well.
In the project directory use the following commands:
For training:
vc train
For testing:
vc eval <model_path> <test_dataset_path>
<model_path> is the path to the saved pytorch model. One such model is in the
classifier/results/
directory
<test_dataset_path> is the path to a directory containing the test images.
The file config.yaml
stores the configuration of the classifier.
Used the InceptionV3 network for fine-tuning on the dataset hosted at https://www.kaggle.com/c/padhai-tamil-vowel-consonant-classification/data
The architecture is shown below:
where X is the input image, X1 are the extracted features which are passed through two classifiers for detecting the Vowel and the consonants. The number of Vowels in Tamil language are:13 and the consonant count is 19.
Some samples from the training dataset are shown in the figure below. The training data used is a synthetic dataset.
The test set accuracy on the dataset from this method is around 94.04% on a test set size of around 10000 samples.
- Compute the accuracy after introducing Mixup data augmentation and compute the results.
- Try out other model architectures like ResNeXT, WideResNet and ResNet-152 and compare the results
Kushagra Pandey / @kpandey008