{ "info": { "author": "dansuh17", "author_email": "kaehops@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "# PyTorch Land\n\nThis is a repository of select implementations of deep learning models using [pytorch](https://pytorch.org/). \n\nThere is also my own mini-framework for model training \n(which I call simply `NetworkTrainer`), that ended up with \nsomething very similar to [ignite](https://pytorch.org/ignite/), \ncreated in order to reduce common boilerplating.\nMore flexible and intuitive than ignite, in my opinion :).\n\nMore models to be added, and improvements on `NetworkTrainer` is under way.\n\n## Installation\n\n```bash\npip3 install pytorch-land\n```\n\nSee [pypi page](https://pypi.org/project/pytorch-land/) for package details.\n\n## Implemented Models\n\n### CNN architectures\n- ResNet (2015) [[paper](https://arxiv.org/abs/1512.03385)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/resnet)] \n- Inception v.2 (2015) [[paper](https://arxiv.org/abs/1512.00567)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/inception)]\n- MobileNet (2017) [[paper](https://arxiv.org/abs/1704.04861)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/mobilenet)]\n\n### GANs\n- GAN (2014) [[paper](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/gan)]\n- DCGAN (2015) [[paper](https://arxiv.org/abs/1511.06434)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/dcgan)]\n- InfoGAN (2016) [[paper](https://arxiv.org/pdf/1606.03657.pdf)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/infogan)]\n- f-GAN (2016) [[paper](https://arxiv.org/abs/1606.00709)] [[code](https://github.com/deNsuh/pytorch-land/tree/master/fgan)]\n- 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)]\n- 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)]\n- BEGAN (2017) [[paper](https://arxiv.org/abs/1703.10717)] [[code & examples](https://github.com/deNsuh/pytorch-land/tree/master/began)]\n- CycleGAN (2017) [[paper](https://arxiv.org/abs/1703.10593)] [[code & examples](https://github.com/deNsuh/pytorch-land/tree/master/cyclegan)]\n\n### Autoencoders\n- 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)]\n- 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)]\n\n## Requirements\n\nRequired packages are specified in [requirements.txt](https://github.com/deNsuh/pytorch-land/blob/master/requirements.txt)\nfile. The packages can be installed using the following command:\n\n```bash\npip3 install -r requirements.txt\n```\n\nNotably, the codes are compatible with **pytorch 0.4** - working on with **pytorch 1.1** compatibility.\n\n## NetworkTrainer\n\n## Datasets", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/dansuh17/pytorch-land", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "pytorch-land", "package_url": "https://pypi.org/project/pytorch-land/", "platform": "", "project_url": "https://pypi.org/project/pytorch-land/", "project_urls": { "Homepage": "https://github.com/dansuh17/pytorch-land" }, "release_url": "https://pypi.org/project/pytorch-land/0.1.6/", "requires_dist": null, "requires_python": "", "summary": "pytorch-land for happy deep learning", "version": "0.1.6", "yanked": false, "yanked_reason": null }, 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