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deep-text-recognition-benchmark

Text recognition (optical character recognition) with deep learning methods.

deeplab-pytorch

PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

deeplabv3plus-pytorch

DeepLabv3, DeepLabv3+ and pretrained weights on VOC & Cityscapes

deeplabv3_pytorch

基于矿区无人机影像的地物提取实验

deformable-convnets-caffe

Deformable Convolutional Networks on caffe

deformable-convolution-v2-pytorch

Deformable ConvNets V2 (DCNv2) in PyTorch

differentiablebinarization

DB (Real-time Scene Text Detection with Differentiable Binarization) implementation in Keras and Tensorflow

dl-notes-for-interview

deep learning/ machine learning

edgeai-torchvision

Datasets, Transforms and Models specific to Computer Vision

external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

hrnet-semantic-segmentation

This is an official implementation of semantic segmentation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919

imagenet_resnet_tensorflow2.0

Train ResNet on ImageNet in Tensorflow 2.0; ResNet 在ImageNet上完整训练代码

keras-resnet

Residual networks implementation using Keras-1.0 functional API

learning-cuda-trt

A large number of cuda/tensorrt cases . 大量案例来学习cuda/tensorrt

lede

Lean's OpenWrt source

libtorch-yolov5

A LibTorch inference implementation of the yolov5

libtorch_centernet

libtorch1.1.0，centernet，dla34-nodcn

libtorch_refinedet

RefineDet_API_for_pytorch C++

libtorch_refinedet_2020

libtorch_RefineDet

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mlapp_cn_code

《Machine Learning: A Probabilistic Perspective》（Kevin P. Murphy）中文翻译和书中算法的Python实现。

mmdetection_libtorch

mmdetection in python训练， libtorch in c++部署

mobilenet-ssd-tensorrt

Accelerate mobileNet-ssd with tensorRT

model-compression-deploy

model compression and deploy. compression: 1、quantization: quantization-aware-training, 16/8/4/2-bit(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net); post-training-quantization, 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization folding for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

multi-label-classification

基于tf.keras的多标签多分类模型

multi-line-plate-pytorch

training code of multi-line-plate-recognition

multi-line-plate-recognition

Multi-line license plate recognition

network-slimming

Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

new-yolov1_pytorch