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attention_module

awesome-programming-books-1

📚 经典技术书籍 PDF 文件，持续更新...

c-_learn

data-science-notes

数据科学的笔记以及资料搜集

dcgan-mnist

利用简单DCGAN生成手写数字图像

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deepcompression-pytorch-master

deeplearningsystem

Deep Learning System core principles introduction.

deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

densebox

Implemention of Baidu's DenseBox used for multi-task learning of object detection and landmark(key-point) localization 用PyTorch实现了百度的DenseBox并针对任意宽高比矩形(不仅限于方形)的目标检测做了优化，不仅可以输出关键点的热力图(heatmap)而且可以输出每个bbox对应关键点坐标

distill_demo

eastmoney_spider

爬取东方财富网上市公司的财务报表数据

edit_onnx

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

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、regula

mmdetection_sa

OpenMMLab Detection Toolbox and Benchmark

model_deployment

sample model deployment code

model_quantization

mqbench

Model Quantization Benchmark

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

new-yolov1_pytorch

next-prediction

Code and model for "Peeking into the Future: Predicting Future Person Activities and Locations in Videos", Liang et al, CVPR 2019

plotneuralnet

Latex code for making neural networks diagrams

python_demo

pytorch-quantization-demo

A simple network quantization demo using pytorch from scratch.

pytorch_yolo-family

pytorch_yolov1

A new version of YOLOv1

qdrop

sgan

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

social-lstm

Social LSTM implementation in PyTorch