Contents
How would you prepare a dataset for deep learning?
Preparing Your Dataset for Machine Learning: 10 Basic Techniques That Make Your Data Better
- Articulate the problem early.
- Establish data collection mechanisms.
- Check your data quality.
- Format data to make it consistent.
- Reduce data.
- Complete data cleaning.
- Decompose data.
- Join transactional and attribute data.
How do you collect image dataset for deep learning?
A simple way to collect your deep learning image dataset
- Support file type filters.
- Support Bing.com filterui filters.
- Download using multithreading and custom thread pool size.
- Support purely obtaining the image URLs.
How to create a deep learning image dataset?
Image data for Deep Learning models should be either a numpy array or a tensor object. Each class is a folder containing images for that particular class. The source folder is the input parameter containing the images for different classes. Read the image file from the folder and convert it to the right color format.
Which is image resolution should I use for training for deep?
I also think it’s better to look at the average size of all the images, taking an average size and image rotations can help you make a good training. Use mean and std to decide shrinking/zooming so that all input images have the same number of pixels.
How to use deep learning for image restoration?
This article describes the techniques and training a deep learning model for image improvement, image restoration, inpainting and super resolution. This utilises many techniques taught in the Fastai course and makes use of the Fastai software library.
How to use deep learning for super resolution?
Deep learning based super resolution, without using a GAN | by Christopher Thomas BSc Hons. MIAP | Towards Data Science Christopher Thomas BSc Hons. MIAP This article describes the techniques and training a deep learning model for image improvement, image restoration, inpainting and super resolution.