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# Modern Computer Vision with PyTorch
This is the code repository for [Modern Computer Vision with PyTorch](https://www.packtpub.com/product/modern-computer-vision-with-pytorch/9781839213472?utm_source=github&utm_medium=repository&utm_campaign=9781839213472), published by Packt.
**Explore deep learning concepts and implement over 50 real-world image applications**
## What is this book about?
Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.
By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world computer vision problems confidently.
This book covers the following exciting features:
* Train a NN from scratch in NumPy and then in PyTorch
* Implement 2D and 3D multi-object detection and segmentation
* Generate digits and DeepFakes with autoencoders and advanced (GANs)
* Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
* Combine CV with natural language processing to perform OCR, image captioning, and object detection
* Combine CV with reinforcement learning to build agents that play pong and self-drive a car
* Deploy a deep learning model on the AWS server using FastAPI and Docker
* Implement over 35 NN architectures and common OpenCV utilities
If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1839213477) today!
## Missing Datasets
Some of datasets were removed from their original links by authors, if you find them missing, please raise an issue and we'll track it down and place them at this huggingface datsets link
## Errata
- [Chapter01](Chapter01/readme.md)
- [Chapter03](Chapter03/Readme.md)
- [Chapter06](Chapter06/Readme.md)
Please use `%pip install torch-snippets==0.539` in any of the codes if there's an issue with torch-snippets library as it was the library version during the release of the notebook.
## Instructions and Navigations
All of the code is organized into folders.
The code will look like the following:
```
def accuracy(x, y, model):
model.eval() # <- let's wait till we get to dropout section
# get the prediction matrix for a tensor of `x` images
prediction = model(x)
# compute if the location of maximum in each row coincides
# with ground truth
max_values, argmaxes = prediction.max(-1)
is_correct = argmaxes == y
return is_correct.cpu().numpy().tolist()
```
**Following is what you need for this book:**
This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-18).
### Software and Hardware List
| Chapter | Software required | OS required |
| -------- | -------------------------------------------------------------------------------------| -----------------------------------|
| 1 - 18 | Minimum 8 GB RAM, Intel i5 processor or better | Windows, Mac OS X, and Linux (Any) |
| | NVIDIA 8+ GB graphics card – GTX1070 or better | |
| | Minimum 50 Mbps internet speed | |
| | Python 3.6 and above | |
| | PyTorch 1.7 | |
| | Google Colab (can run in any browser) | |
All the notebooks can be run directly on [colab](https://colab.google.com) using the [](https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch/) button that can be found at the start of every notebook.
If you wish to run the notebooks locally, ensure you have a CUDA compatible GPU with drivers installed. Instructions are given [here](https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Install-CUDA-Drivers.md)
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://static.packt-cdn.com/downloads/9781839213472_ColorImages.pdf).
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