Metadata-Version: 2.1
Name: pytorch-land
Version: 0.1.2
Summary: pytorch-land for happy deep learning
Home-page: https://github.com/dansuh17/pytorch-land
Author: dansuh17
Author-email: kaehops@gmail.com
License: MIT
Description: # PyTorch Land
        
        This is a repository of select implementations of deep learning models using [pytorch](https://pytorch.org/). 
        
        There is also my own mini-framework for model training 
        (which I call simply `NetworkTrainer`), that ended up with 
        something very similar to [ignite](https://pytorch.org/ignite/), 
        created in order to reduce common boilerplating.
        More flexible and intuitive than ignite, in my opinion :).
        
        More models to be added, and improvements on `NetworkTrainer` is under way.
        
        ## Installation
        
        ```bash
        pip3 install pytorch-land
        ```
        
        See [pypi page](https://pypi.org/project/pytorch-land/) for package details.
        
        ## Implemented Models
        
        ### CNN architectures
        - ResNet (2015) [[paper](https://arxiv.org/abs/1512.03385)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/resnet)] 
        - Inception v.2 (2015) [[paper](https://arxiv.org/abs/1512.00567)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/inception)]
        - MobileNet (2017) [[paper](https://arxiv.org/abs/1704.04861)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/mobilenet)]
        
        ### GANs
        - GAN (2014) [[paper](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/gan)]
        - DCGAN (2015) [[paper](https://arxiv.org/abs/1511.06434)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/dcgan)]
        - InfoGAN (2016) [[paper](https://arxiv.org/pdf/1606.03657.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/infogan)]
        - f-GAN (2016) [[paper](https://arxiv.org/abs/1606.00709)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/fgan)]
        - UnrolledGAN (2016) [[paper](https://arxiv.org/abs/1611.02163)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/unrolled_gan)] [[train-examples](https://github.com/deNsuh/pytorch-land/blob/master/unrolled_gan/unrolledgan_train_results.ipynb)]
        - ACGAN (2016) [[paper](https://arxiv.org/abs/1610.09585)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/acgan)] [[train-examples](https://github.com/deNsuh/pytorch-land/tree/master/acgan)]
        - BEGAN (2017) [[paper](https://arxiv.org/abs/1703.10717)] [[code & examples](https://github.com/deNsuh/pytorch-land/tree/master/began)]
        - CycleGAN (2017) [[paper](https://arxiv.org/abs/1703.10593)] [[code & examples](https://github.com/deNsuh/pytorch-land/tree/master/cyclegan)]
        
        ### Autoencoders
        - Stacked Denoising Autoencoders [[paper](https://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/sdae)]
        - Stacked Convolutional Denoising Autoencoders (2017) [[paper](https://mediatum.ub.tum.de/doc/1381852/54858742554.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/schmidt_sda)]
        
        ## Requirements
        
        Required packages are specified in [requirements.txt](https://github.com/deNsuh/pytorch-land/blob/master/requirements.txt)
        file. The packages can be installed using the following command:
        
        ```bash
        pip3 install -r requirements.txt
        ```
        
        Notably, the codes are compatible with **pytorch 0.4** - working on with **pytorch 1.1** compatibility.
        
        ## NetworkTrainer
        
        ## Datasets
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
