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https://github.com/sushantdhumak/dog-breed-classifier
Deep Learning Nanodegree Project : Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied with an image of a human, the code will identify the resembling dog breed.
https://github.com/sushantdhumak/dog-breed-classifier
accuracy bottleneck-features categorical-cross-entropy cnn-architecture convolutional-neural-network deep-neural-networks detect-faces dog-breed dog-breed-classifier keras keras-neural-networks preprocessing resnet-50 rmsprop transfer-learning validation-loss vgg16 xception-model
Last synced: 3 days ago
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Deep Learning Nanodegree Project : Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied with an image of a human, the code will identify the resembling dog breed.
- Host: GitHub
- URL: https://github.com/sushantdhumak/dog-breed-classifier
- Owner: sushantdhumak
- Created: 2019-11-10T11:44:34.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-10T11:56:36.000Z (about 5 years ago)
- Last Synced: 2025-01-01T18:34:24.120Z (about 1 month ago)
- Topics: accuracy, bottleneck-features, categorical-cross-entropy, cnn-architecture, convolutional-neural-network, deep-neural-networks, detect-faces, dog-breed, dog-breed-classifier, keras, keras-neural-networks, preprocessing, resnet-50, rmsprop, transfer-learning, validation-loss, vgg16, xception-model
- Language: Jupyter Notebook
- Homepage:
- Size: 1.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Project: Dog-Breed-Classifier
## Deep Learning Nanodegree
### Disclaimer:
The given solutions in this project are only for reference purpose.
### Kaggle Competition Link:
https://www.kaggle.com/c/dog-breed-identification
## Project Overview
In this project, we will learn how to build 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, the 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.
Along with exploring state-of-the-art CNN models for classification, we will make important design decisions about the user experience for the app. Our goal is that by completing this lab, we will understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Our imperfect solution will nonetheless create a fun user experience!
## Project Instructions
### Instructions
1. Clone the repository and navigate to the downloaded folder.
```
git clone https://github.com/udacity/dog-project.git
cd dog-project
```
2. Download the [dog dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip). Unzip the folder and place it in the repo, at location `path/to/dog-project/dogImages`.
3. Download the [human dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip). Unzip the folder and place it in the repo, at location `path/to/dog-project/lfw`. If you are using a Windows machine, you are encouraged to use [7zip](http://www.7-zip.org/) to extract the folder.
4. Download the [VGG-16 bottleneck features](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG16Data.npz) for the dog dataset. Place it in the repo, at location `path/to/dog-project/bottleneck_features`.
5. (Optional) __If you plan to install TensorFlow with GPU support on your local machine__, follow [the guide](https://www.tensorflow.org/install/) to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step.
6. (Optional) **If you are running the project on your local machine (and not using AWS)**, create (and activate) a new environment.
- __Linux__ (to install with __GPU support__, change `requirements/dog-linux.yml` to `requirements/dog-linux-gpu.yml`):
```
conda env create -f requirements/dog-linux.yml
source activate dog-project
```
- __Mac__ (to install with __GPU support__, change `requirements/dog-mac.yml` to `requirements/dog-mac-gpu.yml`):
```
conda env create -f requirements/dog-mac.yml
source activate dog-project
```
**NOTE:** Some Mac users may need to install a different version of OpenCV
```
conda install --channel https://conda.anaconda.org/menpo opencv3
```
- __Windows__ (to install with __GPU support__, change `requirements/dog-windows.yml` to `requirements/dog-windows-gpu.yml`):
```
conda env create -f requirements/dog-windows.yml
activate dog-project
```
7. (Optional) **If you are running the project on your local machine (and not using AWS)** and Step 6 throws errors, try this __alternative__ step to create your environment.
- __Linux__ or __Mac__ (to install with __GPU support__, change `requirements/requirements.txt` to `requirements/requirements-gpu.txt`):
```
conda create --name dog-project python=3.5
source activate dog-project
pip install -r requirements/requirements.txt
```
**NOTE:** Some Mac users may need to install a different version of OpenCV
```
conda install --channel https://conda.anaconda.org/menpo opencv3
```
- __Windows__ (to install with __GPU support__, change `requirements/requirements.txt` to `requirements/requirements-gpu.txt`):
```
conda create --name dog-project python=3.5
activate dog-project
pip install -r requirements/requirements.txt
```
8. (Optional) **If you are using AWS**, install Tensorflow.
```
sudo python3 -m pip install -r requirements/requirements-gpu.txt
```
9. Switch [Keras backend](https://keras.io/backend/) to TensorFlow.
- __Linux__ or __Mac__:
```
KERAS_BACKEND=tensorflow python -c "from keras import backend"
```
- __Windows__:
```
set KERAS_BACKEND=tensorflow
python -c "from keras import backend"
```
10. (Optional) **If you are running the project on your local machine (and not using AWS)**, create an [IPython kernel](http://ipython.readthedocs.io/en/stable/install/kernel_install.html) for the `dog-project` environment.
```
python -m ipykernel install --user --name dog-project --display-name "dog-project"
```
11. Open the notebook.
```
jupyter notebook dog_app.ipynb
```
12. (Optional) **If you are running the project on your local machine (and not using AWS)**, before running code, change the kernel to match the dog-project environment by using the drop-down menu (**Kernel > Change kernel > dog-project**). Then, follow the instructions in the notebook.
__NOTE:__ While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. __Unless requested, do not modify code that has already been included.__