174614361

Pinned Repositories

ArithmeticCoding
Image compression using arithmetic coding algorithm
Language:Python10
caffe
Caffe: a fast open framework for deep learning.
Language:C++00
caffe-fast-rcnn
Caffe fork that supports Fast R-CNN
Language:C++00
caffe-fcn
Run Long and Shelhamer's FCN image segmentation network using Caffe
Language:Jupyter Notebook00
caffe-model
Caffe models (imagenet pretrain) and prototxt generator scripts for inception_v3 \ inception_v4 \ inception_resnet \ fractalnet \ resnext
Language:Python00
caffe-model-1
Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
Language:Python00
caffe-rfcn
Caffe branch for R-FCN
Language:C++00
CBAM.PyTorch
Non-official implement of Paper：CBAM: Convolutional Block Attention Module
Language:Python00
cifar10-tensorflow
cifar10数据集上进行图片分类，基于tensorflow框架，旨在探究不同的改进策略对分类准确率的影响，如何一步步得提高准确率
Language:Python00
Urinary-Sediment-Dataset
a dataset consisting of 5,376 annotated images corresponding to 7 categories of urinary particle
14 3 15

174614361's Repositories

174614361/Urinary-Sediment-Dataset
a dataset consisting of 5,376 annotated images corresponding to 7 categories of urinary particle
14 3 15
174614361/ArithmeticCoding
Image compression using arithmetic coding algorithm
Language:Python10
174614361/caffe
Caffe: a fast open framework for deep learning.
Language:C++00
174614361/CBAM.PyTorch
Non-official implement of Paper：CBAM: Convolutional Block Attention Module
Language:Python00
174614361/deep-high-resolution-net.pytorch
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
174614361/deeplab-pytorch
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC
174614361/DenseNet-Caffe
DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet
174614361/efficient_densenet_pytorch
A memory-efficient implementation of DenseNets
174614361/FPN_Faster_RCNN
a tensorflow implementation for FPN
Language:Python
174614361/Huffman-Coding
信息论大作业
174614361/Involution
PyTorch reimplementation of the paper "Involution: Inverting the Inherence of Convolution for Visual Recognition" (2D and 3D Involution) [CVPR 2021].
174614361/involution_pytorch
Unofficial PyTorch implementation of the Involution layer from CVPR 2021
174614361/keras-applications
Reference implementations of popular deep learning models.
Language:Python
174614361/KNEEL
Hourglass Networks for Knee Anatomical Landmark Localization: PyTorch Implementation
174614361/ML_Practice
ML Records in 1110 Lab of BUPT. Some detailed information can be referenced on: https://mathpretty.com/10388.html
174614361/MobileNet-Caffe
Caffe Implementation of Google's MobileNets (v1 and v2)
Language:Python
174614361/pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
174614361/pytorch-mobilenet-v2
A PyTorch implementation of MobileNet V2 architecture and pretrained model.
174614361/PyTorch-Tutorial
Build your neural network easy and fast
174614361/SENet
Squeeze-and-Excitation Networks
Language:Cuda
174614361/SENet-Caffe
A Caffe Re-Implementation of SENet
174614361/SKNet
Code for our CVPR 2019 paper: Selective Kernel Networks; See zhihu：https://zhuanlan.zhihu.com/p/59690223
174614361/SNIPER-mxnet
MXNet fork compatible with SNIPER object detector
Language:Python
174614361/sparse-to-dense.pytorch
ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation)
174614361/tutorials
机器学习相关教程
174614361/vehicle-license-plate-recognition
:fire: :fire::fire:基于Python的车牌检测和识别系统：
174614361/vision
Datasets, Transforms and Models specific to Computer Vision
174614361/Xception-caffe
Xception implemented with caffe
174614361/yolov5
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
174614361/YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~