Displaying 1 to 20 from 50 results

chainer - A flexible framework of neural networks for deep learning

  •    Python

Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.

espnet - End-to-End Speech Processing Toolkit

  •    Shell

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition, and end-to-end text-to-speech. ESPnet uses chainer and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

deepo - A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment

  •    Python

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g. This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

Deep-Learning-Project-Template - A best practice for deep learning project template architecture.

  •    Python

A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in pytorch projects here's a pytorch project template that combines simplicity, best practice for folder structure and good OOP design. The main idea is that there's much same stuff you do every time when you start your pytorch project, so wrapping all this shared stuff will help you to change just the core idea every time you start a new pytorch project. In order to decrease repeated stuff, we recommend to use a high-level library. You can write your own high-level library or you can just use some third-part libraries such as ignite, fastai, mmcv … etc. This can help you write compact but full-featured training loops in a few lines of code. Here we use ignite to train mnist as an example.

chainercv - ChainerCV: a Library for Deep Learning in Computer Vision

  •    Python

ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. You can find the documentation here.

deeppose - DeepPose implementation in Chainer

  •    Python

NOTE: This is not official implementation. Original paper is DeepPose: Human Pose Estimation via Deep Neural Networks. I strongly recommend to use Anaconda environment. This repo may be able to be used in Python 2.7 environment, but I haven't tested.

chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.

  •    Python

ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. ChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see requirements.txt.

chainer-faster-rcnn - Object Detection with Faster R-CNN in Chainer

  •    Python

This is an experimental implementation of Faster R-CNN in Chainer based on Ross Girshick's work: py-faster-rcnn codes. Using anaconda is strongly recommended.


  •    Python

Implementation of "A neural algorithm of Artistic style" (http://arxiv.org/abs/1508.06576) in Chainer. The Japanese readme can be found here.

test-tube - Python library to easily log, track machine learning code, experiments and parallelize hyperparameter search

  •    HTML

Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

attention_is_all_you_need - Transformer of "Attention Is All You Need" (Vaswani et al

  •    Jupyter

Chainer-based Python implementation of Transformer, an attention-based seq2seq model without convolution and recurrence. If you want to see the architecture, please see net.py. See "Attention Is All You Need", Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017.

chainer_pong - learn pong by chainer

  •    Python

DQN implementation by Chainer. It iterators 5 episode. If you store the model on ./store directory, that is loaded. You can use trained model that are located in trained_model directory (it is stored by Git LFS, storing latest 5 model). Please copy it to /store directory then run script.

chainer-pspnet - PSPNet in Chainer

  •    Python

This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Caffe is NOT needed to convert .caffemodel to Chainer model. Use caffe_pb2.py.

chainer2pytorch - Converts Chainer modules to PyTorch, parameters included.

  •    Python

chainer2pytorch implements conversions from Chainer modules to PyTorch modules, setting parameters of each modules such that one can port over models on a module basis. Note that when do you a forward call, PyTorch's LSTM only gives the output of the last layer, whereas chainer gives the output of all layers.

onnx-chainer - Add-on package for ONNX format support in Chainer

  •    Python

This is an add-on package for ONNX support by Chainer. Using onnx-caffe2 is a simple way to do it.

superresolution_gan - Chainer implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

  •    Python

Chainer implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

sagemaker-chainer-container - Docker container for running Chainer scripts to train and host Chainer models on SageMaker

  •    Python

SageMaker Chainer Containers is an open source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.