amaanda

amaanda's Stars

• albumentations-team/albumentations

  Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

  Language:Python14.2k1.6k

• JialeCao001/D2Det

  D2Det: Towards High Quality Object Detection and Instance Segmentation (CVPR2020)

  Language:Python29686

• tensorflow/models

  Models and examples built with TensorFlow

  Language:Python77.1k45.8k

• JialeCao001/SipMask

  SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)

  Language:Jupyter Notebook33554

• PaddlePaddle/PaddleDetection

  Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

  Language:Python12.8k2.9k

• tensorflow/tpu

  Reference models and tools for Cloud TPUs.

  Language:Jupyter Notebook5.2k1.8k

• whdcumt/BlurDetection

  A Python-Based Blur Detector using Fast Fourier Transforms

  Language:Python12729

• mseg-dataset/mseg-api

  An Official Repo of CVPR '20 "MSeg: A Composite Dataset for Multi-Domain Segmentation"

  Language:Python24944

• valeoai/ZS3

  Zero-Shot Semantic Segmentation

  Language:Python18726

• aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples

  A collection of sample scripts to customize Amazon SageMaker Notebook Instances using Lifecycle Configurations

  Language:Shell420250

• mariokostelac/sagemaker-setup

  Useful scripts for making AWS SageMaker better

  Language:Shell2318

• itsasimiqbal/DeNeRD

  High-throughput Detection of Neurons for Brain-wide analysis with Deep Learning

  Language:MATLAB172

• petewarden/tensorflow_makefile

  Language:C++69114

• DeepLabCut/DeepLabCut-Workshop-Materials

  Workshop material for using DeepLabCut

  Language:Jupyter Notebook13936

• aws/deep-learning-containers

  AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.

  Language:Python1k463

• ChandraLingam/AmazonSageMakerCourse

  In this AWS Machine Learning Specialty Course, You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud. Learn how to use AWS Built-in SageMaker algorithms and AI, How to Bring Your Own Algorithm, Zero Downtime Model Deployment Options, How to Integrate and Invoke ML from your Application, Automated Hyperparameter Tuning

  Language:Jupyter Notebook228393

• aws/amazon-sagemaker-examples

  Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

  Language:Jupyter Notebook10.1k6.8k

• Tramac/awesome-semantic-segmentation-pytorch

  Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

  Language:Python2.9k582

• yuanming-hu/exposure

  Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model.

  Language:Python764155

• shashankprasanna/sagemaker-spot-training

  Language:Python10

• facebook/zstd

  Zstandard - Fast real-time compression algorithm

  Language:C23.6k2.1k

• ankitdhall/lidar_camera_calibration

  ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

  Language:C++1.5k461

• pjreddie/darknet

  Convolutional Neural Networks

  Language:C25.8k21.3k

• yizhou-wang/darknet-kitti

  Revised version of YOLOv2 and YOLOv3 for KITTI dataset

  Language:C399

• xuanyuzhou98/SqueezeSegV2

  Implementation of SqueezeSegV2, Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

  Language:Python23369

• ialhashim/DenseDepth

  High Quality Monocular Depth Estimation via Transfer Learning

  Language:Jupyter Notebook1.6k353

• mrharicot/monodepth

  Unsupervised single image depth prediction with CNNs

  Language:Python2.2k632

• MankaranSingh/Auto-Depth

  3D Reconstruction / Pseudo LiDAR via Deep Learning

  Language:Python9915

• UnaNancyOwen/VelodyneCapture

  VelodyneCapture is the general capture class to retrieve the laser data from Velodyne sensors using Boost.Asio and PCAP

  Language:C++5421

• Kitware/VeloView

  VeloView performs real-time visualization and easy processing of live captured 3D LiDAR data from Velodyne sensors (Alpha Prime™, Puck™, Ultra Puck™, Puck Hi-Res™, Alpha Puck™, Puck LITE™, HDL-32, HDL-64E). Runs on Windows, Linux and MacOS. This repository is a mirror of https://gitlab.kitware.com/LidarView/VeloView-Velodyne.

  Language:C++316165