A README documentation file which briefly describes the software and libraries used in your project, including any necessary references to supporting material. If your project requires setup/startup, ensure that your README includes the necessary instructions.
Softwares used: 1.Python 3.6 programming language 2.Jupyter 3.Anaconda package manager
Libraries:
- Numpy
- glob
- OpenCV
- matplotlib
- Tqdm
- Pytorch and Torchvision
- PIL(Python Imaging Library)
- OS
Supporting materials:
- Dog dataset:https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip
- Human dataset:http://vis-www.cs.umass.edu/lfw/lfw.tgz
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
- Liu, Jiongxin, et al. "Dog breed classification using part localization." European conference on computer vision. Springer, Berlin, Heidelberg, 2012.
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Cuimei, Li, et al. "Human face detection algorithm via Haar cascade classifier combined with three additional classifiers." 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, 2017.
- https://neurohive.io/en/popular-networks/vgg16/
- https://iq.opengenus.org/resnet50-architecture/
- https://medium.com/@erika.dauria/accuracy-recall-precision-80a5b6cbd28d#:~:text=Accuracy%20is%20an%20evaluation%20metric,True%20Positives%20and%20True%20Negatives.
-
Emami, Shervin, and Valentin Petrut Suciu. "Facial recognition using OpenCV." Journal of Mobile, Embedded and Distributed Systems 4.1 (2012): 38-43.
Setup:
-
Clone the repository and navigate to the downloaded folder.
git clone https://github.com/udacity/deep-learning-v2-pytorch.git cd deep-learning-v2-pytorch/project-dog-classification
-
Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. The dogImages/ folder should contain 133 folders, each corresponding to a different dog breed.
-
Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw.
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Make sure you have already installed the necessary Python packages according to the README in the program repository.
-
Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.
jupyter notebook dog_app.ipynb