What is SmallTrain?
| SmallTrain is a wrapper able to use TensorFlow and Pytorch, It’s designed as a hi-accuracy model. |
| SmallTrain is a deep learning library adopted the-state-of-the-art algorithms from the newest paper and our own algorithms. |
| SmallTrain is a platform where you can easily build your own trained model with additional learning with your small data can create your high-accuracy model with only a few lines of programming is required. |
| See the different sections of the documentation and site for more ideas. |
Ways to create AI models:
| Ways to create AI models: |
| - Build your model by learning all the data science algorithms and mathematics. |
| - Build using libraries such as Tensorflow that does the algorithms and mathematical part |
| - Build using wrappers that use the library for ease of development(for rapid development) |
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| The easiest of these three ways is to use the third wrapper. You can use SmallTrain as this wrapper. |
How are we different from Keras and other wrappers library?
| - Keras and other wrapper is suitable for PoC but in complex problems it fails and as a wrapper it has a lot of limitations, whereas SmallTrain is a wrapper that closely relates with models in architecture level but still maintaining the ease of development that Keras and other wrapper provides. |
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| - SmallTrain uses a base neural network of about 100 layers of pyramid architecture neural network build referring to state-of-the-art papers and implemented the cutting edge algorithms. |
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| - SmallTrain supports Tensorflow and PyTorch and is designed in a way that they both are non-intrusive with each other and in many cases aids in development according to the style that user needs. |
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| - SmallTrain will train quickly with the data that you can provide and it is highly versatile and supports all kinds of data, and evolves with various range of data. |
How to Use SmallTrain
See the different sections of the documentation and site for more ideas.
Great features of SmallTrain
| - Easy to develop for POC to production. |
| - Almost no programming for building your pre-trained model. |
| - Available as both TensorFlow and Pytorch wrapper. |
| - Always adapting to algorithms which evolve. |
| - Build using state-of-the-art Algorithms from Scientific and mathematical papers. |
| - Accuracy is always going to be better even with minimal data and training time. |
| - Licensed under MIT Open Source. |
SmallTrain Problem space bubble
| - Image recognition |
| - Time series data forcasting |
| - Object detection |
| - Anomaly detection |
| - Voice recognition |