Implementation of Convolutional Neural Networks using Dog Identification App
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling
Final Project Notebook
Download Anaconda
| 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 Anaconda on your machine. Detailed instructions:
Please go though this doc before you creating an environment. After that create a environment using following command
conda create --name deep-learning
Then activate the environment using following command
activate deep-learning
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
Now, you can create a local version of the project
- 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/koushik-elite/Dog-Identification-App.git
cd TV-Script-Generation
-
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 -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 run the project using following command, check you default browser and open dog_app.ipynb file
jupyter notebook