Introduction to Computer Vision
This repository contains notebooks for learning about how convolutional neural networks (CNN's) can be used to create an image classifier given a set of training data. The notebooks include examples that are meant for learning about the individual layers that make up a convolutional neural network, and an example clothing classifier that is trained on the FashionMNIST dataset.
Setting up Your Own Environment
These notebooks depend on a number of software packages to run, and if you want (on your own time), you can create a local environment with these dependencies by following the instructions below.
Configure and Manage Your Environment with Anaconda
Per the Anaconda docs:
Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.
Overview
Using Anaconda consists of the following:
- Install
minicondaon your computer, by selecting the latest Python version for your operating system. If you already havecondaorminicondainstalled, you should be able to skip this step and move on to step 2. - Create and activate * a new
condaenvironment.
* Each time you wish to work on any exercises, activate your conda environment!
1. Installation
Download the latest version of miniconda that matches your system.
NOTE: There have been reports of issues creating an environment using miniconda v4.3.13. If it gives you issues try versions 4.3.11 or 4.2.12 from here.
| Linux | Mac | Windows | |
|---|---|---|---|
| 64-bit | 64-bit (bash installer) | 64-bit (bash installer) | 64-bit (exe installer) |
| 32-bit | 32-bit (bash installer) | 32-bit (exe installer) |
Install miniconda on your machine. Detailed instructions:
- Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
- Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
- Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install
2. Create and Activate the Environment
For Windows users, these following commands need to be executed from the Anaconda prompt as opposed to a Windows terminal window. For Mac, a normal terminal window will work.
Git and version control
These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first with the command:
conda install git
If you'd like to learn more about version control and using git from the command line, take a look at our free course: Version Control with Git.
Now, we're ready to create our local environment!
- Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
git clone https://github.com/cezannec/intro-computervision.git
cd intro-computervision
-
Create (and activate) a new environment, named
cv-envwith Python 3.6. If prompted to proceed with the install(Proceed [y]/n)type y.- Linux or Mac:
conda create -n cv-env python=3.6 source activate cv-env- Windows:
conda create --name cv-env python=3.6 activate cv-envAt this point your command line should look something like:
(cv-env) <User>:intro-computervision <user>$. The(cv-env)indicates that your environment has been activated, and you can proceed with further package installations. -
Install PyTorch and torchvision; this should install the latest version of PyTorch.
- Linux or Mac:
conda install pytorch torchvision -c pytorch- Windows:
conda install pytorch-cpu -c pytorch pip install torchvision -
Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
pip install -r requirements.txt
- That's it!
Now all of the cv-env libraries are available to you. Assuming you're environment is still activated, you can navigate to the Exercises repo and start looking at the notebooks:
cd
cd intro-computervision
jupyter notebook
To exit the environment when you have completed your work session, simply close the terminal window.
Notes on environment creation and deletion
Verify that the cv-env environment was created in your environments:
conda info --envs
Cleanup downloaded libraries (remove tarballs, zip files, etc):
conda clean -tp
Uninstall the environment (if you want); you can remove it by name:
conda env remove -n cv-env