Tangxin's repositories
BMVCTemplate
Paper template and author instructions for BMVC
micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
alfred
alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046
LeetCode
:monkey:LeetCode、剑指Offer刷题笔记(C/C++、Python3实现)
PaddleOCR
Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices)
imbalanced-dataset-sampler
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
DSFD-Pytorch-Inference
A High-Performance Pytorch Implementation of DSFD for Inference
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
MobileFaceNet_Pytorch
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
flask
The Python micro framework for building web applications.
PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
The-Art-Of-Programming-By-July
本项目曾冲到全球第一,干货集锦见本页面最底部,另完整精致的纸质版《编程之法:面试和算法心得》已在京东/当当上销售
scikit-learn
scikit-learn: machine learning in Python
Keras_DRML
keras code for DRML
caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework.
caffe2_cpp_tutorial
C++ transcripts of the Caffe2 Python tutorials and other C++ example code
FaceDatasets
Some scripts to process face datasets.
transferlearning
Everything about Transfer Learning and Domain Adaptation--迁移学习
Learning_NLP_with_keras
I try to learn deep learning for text and sequences
keras-to-tensorflow
A tutorial on running Keras models in Tensorflow