Efficient PyTorch — Supercharging Training Pipeline

Why reporting only the Top-1 accuracy of your model is often not enough. The final goal of every deep learning project is to bring value to the product.

The final goal of every deep learning project is to bring value to the product. Of course, we want to have the best possible model. What is “best” — depends on the particular use case, and I will leave this discussion aside from this post. I want to talk about how to get the maximum from your train.py script.

In this post we’re going to cover the following tips:

1. High-level frameworks instead own-made train loops
2. Monitor progress of the training with additional metrics
3. Use TensorBoard
4. Visualize predictions of the model
5. Use Dict as return value for Dataset and Model
6. Detect anomalies and address numerical instabilities

Disclaimer: In the next section, I will include a few source-code listings. Most of them are tailored for the [Catalyst_](https://github.com/catalyst-team/catalyst) framework (version 20.08) and available in [pytorch-toolbelt_](https://github.com/BloodAxe/pytorch-toolbelt).

Don’t re-invent the wheel

tips-and-tricks deep-learning pytorch catalyst deep learning

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

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This is a regular classification problem with PyTorch and this is exactly like the one in the previous post of the “PyTorch for Deep Learning” series. The Reason for doing writing the post is for some more reference to classification problem and better understanding.

PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning.

Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.