toe's starred repositories
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
awesome-object-detection
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
deep-text-recognition-benchmark
Text recognition (optical character recognition) with deep learning methods, ICCV 2019
HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
pytorch-beginner
pytorch tutorial for beginners
image-segmentation-keras
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
Single-Image-Super-Resolution
A collection of high-impact and state-of-the-art SR methods
semantic-segmentation
Nvidia Semantic Segmentation monorepo
YOLOv3_TensorFlow
Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
TernausNet
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
thundergbm
ThunderGBM: Fast GBDTs and Random Forests on GPUs
pytorchOCR
基于pytorch的ocr算法库，包括 psenet, pan, dbnet, sast , crnn
YOLOv3_PyTorch
Full implementation of YOLOv3 in PyTorch
Human-Segmentation-PyTorch
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
unet_keras
unet_keras use image Semantic segmentation
CheXNet-Keras
This project is a tool to build CheXNet-like models, written in Keras.
unet-tensorflow-keras
A concise code for training and evaluating Unet using tensorflow+keras
keras-segmentation
Get started with Semantic Segmentation based on Keras, including FCN32/FCN8/SegNet/U-Net
UNet-Tensorflow
A brief tensorflow implementation about UNet.
AI-Benchmark
Build keras models for 9 Tasks in AI-Benchmark: Object Detection: Mobile-v2, Inception-v3, Face Recognition: Inception-Resnet-v1, Super Resolution: SRCNN, VDSR, SRGAN, Resnet-12+GAN, and Semantic Segmantation: ICNet