https://github.com/dansuh17/pytorch-land
[BETA] Easy-to-use pytorch model trainer, dataloader creators, and utilities + my own implementations of select models
https://github.com/dansuh17/pytorch-land
cyclegan deep-learning deep-neural-networks gan generative-adversarial-network generative-model python3 pytorch wgan wgan-gp
Last synced: about 1 month ago
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[BETA] Easy-to-use pytorch model trainer, dataloader creators, and utilities + my own implementations of select models
- Host: GitHub
- URL: https://github.com/dansuh17/pytorch-land
- Owner: dansuh17
- Created: 2018-08-25T12:20:20.000Z (over 6 years ago)
- Default Branch: develop
- Last Pushed: 2023-01-27T01:12:03.000Z (over 2 years ago)
- Last Synced: 2025-03-27T03:23:11.377Z (about 2 months ago)
- Topics: cyclegan, deep-learning, deep-neural-networks, gan, generative-adversarial-network, generative-model, python3, pytorch, wgan, wgan-gp
- Language: Python
- Homepage:
- Size: 6.86 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
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README
# 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