This is part of Udacity Deep Learning Program, and this repository is meant to document my progress towards dog classification project completion.
In this project, I built a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
PyTorch is the deep learning framework used in this project, please follow the following steps to config environment
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.
Using Anaconda consists of the following:
- Install
miniconda
on your computer, by selecting the latest Python version for your operating system. If you already haveconda
orminiconda
installed, you should be able to skip this step and move on to step 2. You can also find achived miniconda version in thislink
, I used Miniconda3-4.5.1-Windows-x86_64 (python 3.6.5) - Create and activate * a new
conda
environment.
- Windows:
conda create --name deep-learning python=3.6.5
conda activate deep-learning
conda install numpy matplotlib pandas jupyter notebook
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch ([cpu version]conda install pytorch-cpu torchvision-cpu -c pytorch)
conda install -c conda-forge opencv
conda install -c conda-forge tqdm