Basic Implementation of machine learning and deep learning algorithms in pytorch
- Alexnet -
alexnet.py - Google Net -
google_net.py - Inception Net V2 -
inception_net_v2.py - Inception Net V3 -
inception_net_v3.py - Lenet5 -
lenet5.py - Mobile Net V1 -
mobile_net_v1.py - Mobile Net V2 -
mobile_net_v2.py - Resnet -
resnet.py - Shuffle Net V1 -
shuffle_net_v1.py - Shuffle Net V2 -
shuffle_net_v2.py - Simple Cnn -
simple_cnn.py - Simple Nn -
simple_nn.py - Squeeze Net -
squeeze_net.py - Vgg16 -
vgg16.py - Xception -
xception.py - Zfnet -
zfnet.py
For each model, you can find the corresponding Python file in the repository. The models are implemented using various deep learning frameworks or libraries such as PyTorch.
To use a particular model, you can download the corresponding Python file and import it into your own project. Make sure you have the necessary dependencies installed for running the models.
To run the code, you'll need the following dependencies:
- torch
- numpy
- matplotlib
- torchsummary
- torchvision
Contributions to this repository are welcome! If you would like to contribute by adding new models, improving existing models, or fixing any issues, feel free to submit a pull request.
Please ensure that your contributions adhere to the repository's coding guidelines and include relevant documentation or usage examples.
This repository is licensed under the MIT License.