
This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pull request to contribute to this list.
Table Of Contents
- Tabular Data
- Tutorials
- Visualization
- Explainability
- Object Detection
- Long-Tailed / Out-of-Distribution Recognition
- Energy-Based Learning
- Missing Data
- Architecture Search
- Optimization
- Quantization
- Quantum Machine Learning
- Neural Network Compression
- Facial, Action and Pose Recognition
- Super resolution
- Synthetesizing Views
- Voice
- Medical
- 3D Segmentation, Classification and Regression
- Video Recognition
- Recurrent Neural Networks (RNNs)
- Convolutional Neural Networks (CNNs)
- Segmentation
- Geometric Deep Learning: Graph & Irregular Structures
- Sorting
- Ordinary Differential Equations Networks
- Multi-task Learning
- GANs, VAEs, and AEs
- Unsupervised Learning
- Adversarial Attacks
- Style Transfer
- Image Captioning
- Transformers
- Similarity Networks and Functions
- Reasoning
- General NLP
- Question and Answering
- Speech Generation and Recognition
- Document and Text Classification
- Text Generation
- Translation
- Sentiment Analysis
- Deep Reinforcement Learning
- Deep Bayesian Learning and Probabilistic Programmming
- Spiking Neural Networks
- Anomaly Detection
- Regression Types
- Time Series
- Synthetic Datasets
- Neural Network General Improvements
- DNN Applications in Chemistry and Physics
- New Thinking on General Neural Network Architecture
- Linear Algebra
- API Abstraction
- Low Level Utilities
- PyTorch Utilities
- PyTorch Video Tutorials
- Datasets
- Community
- Links to This Repository
- To be Classified
- Contributions
1. Tabular Data
2. Tutorials
3. Visualization
4. Explainability
5. Object Detection
6. Long-Tailed / Out-of-Distribution Recognition
7. Energy-Based Learning
8. Missing Data
9. Architecture Search
10. Optimization
- AccSGD, AdaBound, AdaMod, DiffGrad, Lamb, NovoGrad, RAdam, SGDW, Yogi and more
- Lookahead Optimizer: k steps forward, 1 step back
- RAdam, On the Variance of the Adaptive Learning Rate and Beyond
- Over9000, Comparison of RAdam, Lookahead, Novograd, and combinations
- AdaBound, Train As Fast as Adam As Good as SGD
- Riemannian Adaptive Optimization Methods
- L-BFGS
- OptNet: Differentiable Optimization as a Layer in Neural Networks
- Learning to learn by gradient descent by gradient descent
11. Quantization
12. Quantum Machine Learning
13. Neural Network Compression
14. Facial, Action and Pose Recognition
15. Super resolution
16. Synthetesizing Views
17. Voice
18. Medical
19. 3D Segmentation, Classification and Regression
20. Video Recognition
21. Recurrent Neural Networks (RNNs)
22. Convolutional Neural Networks (CNNs)
- LegoNet: Efficient Convolutional Neural Networks with Lego Filters
- MeshCNN, a convolutional neural network designed specifically for triangular meshes
- Octave Convolution
- PyTorch Image Models, ResNet/ResNeXT, DPN, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet
- Deep Neural Networks with Box Convolutions
- Invertible Residual Networks
- Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
- Faster Faster R-CNN Implementation
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
- Wide ResNet model in PyTorch
-DiracNets: Training Very Deep Neural Networks Without Skip-Connections
- An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
- Efficient Densenet
- Video Frame Interpolation via Adaptive Separable Convolution
- Learning local feature descriptors with triplets and shallow convolutional neural networks
- Densely Connected Convolutional Networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- Deep Residual Learning for Image Recognition
- Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch
- Deformable Convolutional Network
- Convolutional Neural Fabrics
- Deformable Convolutional Networks in PyTorch
- Dilated ResNet combination with Dilated Convolutions
- Striving for Simplicity: The All Convolutional Net
- Convolutional LSTM Network
- Big collection of pretrained classification models
- PyTorch Image Classification with Kaggle Dogs vs Cats Dataset
- CIFAR-10 on Pytorch with VGG, ResNet and DenseNet
- Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
- NVIDIA/unsupervised-video-interpolation
23. Segmentation
24. Geometric Deep Learning: Graph & Irregular Structures
25. Sorting
26. Ordinary Differential Equations Networks
27. Multi-task Learning
28. GANs, VAEs, and AEs
29. Unsupervised Learning
30. Adversarial Attacks
31. Style Transfer
32. Image Captioning
34. Similarity Networks and Functions
35. Reasoning
36. General NLP
37. Question and Answering
38. Speech Generation and Recognition
39. Document and Text Classification
40. Text Generation
41. Translation
42. Sentiment Analysis
43. Deep Reinforcement Learning
44. Deep Bayesian Learning and Probabilistic Programmming
45. Spiking Neural Networks
46. Anomaly Detection
47. Regression Types
48. Time Series
49. Synthetic Datasets
50. Neural Network General Improvements
51. DNN Applications in Chemistry and Physics
52. New Thinking on General Neural Network Architecture
53. Linear Algebra
54. API Abstraction
55. Low Level Utilities
56. PyTorch Utilities
- PyTorch Metric Learning
- Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
- BackPACK to easily Extract Variance, Diagonal of Gauss-Newton, and KFAC
- PyHessian for Computing Hessian Eigenvalues, trace of matrix, and ESD
- Hessian in PyTorch
- Differentiable Convex Layers
- Albumentations: Fast Image Augmentation Library
- Higher, obtain higher order gradients over losses spanning training loops
- Neural Pipeline, Training Pipeline for PyTorch
- Layer-by-layer PyTorch Model Profiler for Checking Model Time Consumption
- Sparse Distributions
- Diffdist, Adds Support for Differentiable Communication allowing distributed model parallelism
- HessianFlow, Library for Hessian Based Algorithms
- Texar, PyTorch Toolkit for Text Generation
- PyTorch FLOPs counter
- PyTorch Inference on C++ in Windows
- EuclidesDB, Multi-Model Machine Learning Feature Database
- Data Augmentation and Sampling for Pytorch
- PyText, deep learning based NLP modelling framework officially maintained by FAIR
- Torchstat for Statistics on PyTorch Models
- Load Audio files directly into PyTorch Tensors
- Weight Initializations
- Spatial transformer implemented in PyTorch
- PyTorch AWS AMI, run PyTorch with GPU support in less than 5 minutes
- Use tensorboard with PyTorch
- Simple Fit Module in PyTorch, similar to Keras
- torchbearer: A model fitting library for PyTorch
- PyTorch to Keras model converter
- Gluon to PyTorch model converter with code generation
- Catalyst: High-level utils for PyTorch DL & RL research
- PyTorch Lightning: Scalable and lightweight deep learning research framework
- Determined: Scalable deep learning platform with PyTorch support
- PyTorch-Ignite: High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently
- torchvision: A package consisting of popular datasets, model architectures, and common image transformations for computer vision.
- Poutyne: A Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.
- torchensemble: Scikit-Learn like ensemble methods in PyTorch
57. PyTorch Video Tutorials
58. Datasets
60. Links to This Repository
61. To be Classified
62. Contributions
Do feel free to contribute!
You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.