822 Open source projects that are alternatives of or similar to shellnet

This is an official implementation of our NeurIPS 2021 paper "HRFormer: High-Resolution Transformer for Dense Prediction".

Generalized Data-free Universal Adversarial Perturbations

Image Test Time Augmentation with PyTorch!

All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.

Resurces for MRI images processing and deep learning in 3D

PAPC is a deep learning for point clouds platform based on pure PaddlePaddle

Keras implementation for Pointnet

Grid-GCN for Fast and Scalable Point Cloud Learning

PyTorch Implementation of Stacked U-Nets (SUNets)

Rotation Invariant Convolutions for 3D Point Clouds Deep Learning

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

An open-source semi-automated processing chain for urban OBIA classification.

Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, and clause indexing

deep learning for image processing including classification and object-detection etc.

基于Tensorflow2开发的优秀扩展包及项目

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

Collection of computer vision models, ready to be included in a JVM project

Crop/Weed Field Image Dataset

Tensorflow implementation : U-net and FCN with global convolution

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Collections of model quantization algorithms

Sandbox for training deep learning networks

Papers and Datasets about Point Cloud.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.

All-in-one Toolbox for Computer Vision Research.

A Python toolbox for computer vision research and project

《深度学习与计算机视觉》配套代码

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

Classification and Segmentation with Mask-RCNN of Skin Cancer using ISIC dataset

OpenMMLab Model Compression Toolbox and Benchmark.

segmentation paper reading notes

[ICCV2021] Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP

FCN for Semantic Image Segmentation achieving 68.5 mIoU on PASCAL VOC

A multipurpose tool for medical physics.

Multi-class classification with focal loss for imbalanced datasets

Binary semantic segmentation with UNet based on MobileNetV2 encoder

A Hidden Markov Model implemented in Javascript

Python tools for working with PedX dataset.

Focal Loss of multi-classification in tensorflow

1-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data

Python/R library for feature selection in neural nets. ("Feature selection using Stochastic Gates", ICML 2020)

Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees

Pytorch framework for doing deep learning on point clouds.

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

Superpixel-based semantic segmentation, with object pose estimation and tracking. Provided as a ROS package.

LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

Extremely simple and fast extreme multi-class and multi-label classifiers.

D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn and TPOT.

Multi primitive-to-primitive (MP2P) ICP algorithms in C++

Invariant Superpixel Features for Object Detection

EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

HTTP service for Tensorflow images classification model

XED multilingual emotion datasets