https://github.com/londheshubham153/dog-breed-classifier
https://github.com/londheshubham153/dog-breed-classifier
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/londheshubham153/dog-breed-classifier
- Owner: LondheShubham153
- License: mit
- Created: 2020-05-18T06:33:50.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T01:41:06.000Z (over 2 years ago)
- Last Synced: 2025-01-31T22:06:19.441Z (3 months ago)
- Language: Jupyter Notebook
- Size: 3.44 MB
- Stars: 4
- Watchers: 3
- Forks: 3
- Open Issues: 5
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Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Dog Breeds Classification with CNN Transfer Learning
### Table of Contents
1. [Installation](#installation)
2. [Project Overview](#overview)
3. [File Descriptions](#files)
4. [Results](#results)
5. [Licensing, Authors, and Acknowledgements](#licensing)
Beyond the Anaconda distribution of Python, the following packages need to be installed:
* opencv-python==3.2.0.6
* h5py==2.6.0
* matplotlib==2.0.0
* numpy==1.12.0
* scipy==0.18.1
* tqdm==4.11.2
* scikit-learn==0.18.1
* keras==2.0.2
* tensorflow==1.0.0 `
In this project, I built and trained a neural network model with CNN (Convolutional Neural Networks) transfer learning, using 8351 dog images of 133 breeds. CNN is a type of deep neural networks, which is commonly used to analyze image data. Typically, a CNN architecture consists of convolutional layers, activation function, pooling layers, fully connected layers and normalization layers. Transfer learning is a technique that allows a model developed for a task to be reused as the starting point for another task.
The trained model can be used by a web or mobile application to process real-world, user-supplied images. Given an image of a dog, the algorithm will predict the breed of the dog. If an image of a human is supplied, the code will identify the most resembling dog breed.
Below are main foleders/files for this project:
1. haarcascades
- haarcascade_frontalface_alt.xml: a pre-trained face detector provided by OpenCV
2. bottleneck_features
- DogVGG19Data.npz: pre-computed the bottleneck features for VGG-19 using dog image data including training, validation, and test
3. saved_models
- VGG19_model.json: model architecture saved in a json file
- weights.best.VGG19.hdf5: saved model weights with best validation loss
4. dog_app.ipynb: a notebook used to build and train the dog breeds classification model
5. extract_bottleneck_features.py: functions to compute bottleneck features given a tensor converted from an image
6. images: a few images to test the model manually
Note:
The dog image dataset used by this project can be downloaded here: https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip
The human image dataset can be downloaded here: https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip
1. The model was able to reach an accuracy of 72.97% on test data.
2. If a dog image is supplied, the model gives a prediction of the dog breed.
3. The model is also able to identify the most resembling dog breed of a person.
Project files can be found in this github repo: https://github.com/swang13/dog-breeds-classification
More discussions can be found in this blog: https://medium.com/@wangshuocugb2005/dog-breeds-classification-with-cnn-transfer-learning-92217cba3129
## Licensing, Authors, Acknowledgements
Credits must be given to Udacity for the starter codes and data images used by this project.