GitHub – rdspring1/PyTorch_GBW_LM: PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset – GitHub – rdspring1/PyTorch_GBW_LM: PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset    

@inproceedings{mnih2016asynchronous, title={Asynchronous methods for deep reinforcement learning}, author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy P and Harley, Tim and Silver, David and Kavukcuoglu, Koray}, booktitle={International Conference on Machine Learning}, year={2016}} This repository contains an implementation of Adavantage async Actor-Critic (A3C) in PyTorch based on the original paper by the authors and the PyTorch implementation by Ilya Kostrikov. A3C is the state-of-art Deep Reinforcement Learning method.    

This is a PyTorch implementation of the Transformer model in “Attention is All You Need” (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017). A novel sequence to sequence framework utilizes the self-attention mechanism, instead of Convolution operation or Recurrent structure, and achieve the state-of-the-art performance on WMT 2014 English-to-German translation task. (2017/06/12) The official Tensorflow Implementation can be found in: tensorflow/tensor2tensor. To learn more about self-attention mechanism, you could […]

This project is still in progress. If you are looking for the working code, use BEGAN-tensorflow. Requirements Usage First download CelebA datasets with: $ apt-get install p7zip-full # ubuntu $ brew install p7zip # Mac $ python download.py or you can use your own dataset by placing images like: data └── YOUR_DATASET_NAME ├── xxx.jpg (name doesn’t matter) ├── yyy.jpg └── … To train a model:

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If you’re interested in C++ inference, move HERE Results Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Accuracy 54000 GTX 1080 Ti 512 0.16 100 625 0.9 ~1700 95.65% Sample $ python infer.py -c=./logs/model-54000.pth ./images/test-75.png length: 2 digits: 7 5 10 10 10

Code accompanying the paper “Wasserstein GAN” A few notes The first time running on the LSUN dataset it can take a long time (up to an hour) to create the dataloader. After the first run a small cache file will be created and the process should take a matter of seconds. The cache is a list of indices in the lmdb database (of LSUN) The only addition to the code (that we forgot, and will add, on the paper) are […]

This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in pointnet/model.py. It is tested with pytorch-1.0. git clone https://github.com/fxia22/pointnet.pytorch cd pointnet.pytorch pip install -e . Download and build visualization tool cd script bash build.sh #build C++ code for visualization bash download.sh #download dataset Training cd utils python train_classification.py –dataset –nepoch= –dataset_type python train_segmentation.py –dataset –nepoch= Use –feature_transform to use feature transform. Classification performance On ModelNet40: Overall Acc Original implementation 89.2 this implementation(w/o feature transform) 86.4 this implementation(w/ feature […]

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data. The goal of this library is to make it simple: for machine learning experts to use geospatial data in their workflows, and for remote sensing experts to use their data in machine learning workflows. See our installation instructions, documentation, and examples to learn how to use torchgeo. External links: Tests: Installation instructions Until the first release, you can install […]

This repository contains the training code of our winning model at Music Demixing Challenge 2021, which got the 4th place on leaderboard A (6th in overall), and help us (Kazane Ryo no Danna) winned the bronze prize. Model Summary Our final winning approach blends the outputs from three models, which are: model 1: A X-UMX model [1] which is initialized with the weights of the official baseline, and is fine-tuned with a modified Combinational Multi-Domain Loss from [1]. In particular, […]

Pytorch implementation of BRECQ, ICLR 2021 @inproceedings{ li&gong2021brecq, title={BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction}, author={Yuhang Li and Ruihao Gong and Xu Tan and Yang Yang and Peng Hu and Qi Zhang and Fengwei Yu and Wei Wang and Shi Gu}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=POWv6hDd9XH} } Pretrained models We provide all the pretrained models and they can be accessed via torch.hub For example: use res18 = torch.hub.load(‘yhhhli/BRECQ’, model=’resnet18′, pretrained=True) to get the pretrained ResNet-18 […]