Li Yang's repositories
yolov3
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
BoyNet
Make DL Net Easier.
txt_files
ge zhong xiao shuo
CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++
team-learning-data-mining
主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
Amen
Learning， Embrace the world.
machine_learning_beginner
机器学习初学者公众号作品
libfacedetection
An open source library for face detection in images. The face detection speed can reach 1000FPS.
alfred
alfred-py: A deep learning utility library for visualization and sensor fusion purpose
python-small-examples
Python有趣的小例子一网打尽。Python基础、Python坑点、Python字符串和正则、Python绘图、Python日期和文件、Web开发、数据科学、机器学习、深度学习、TensorFlow、Pytorch，一切都是简单易懂的小例子。
Github_store
git下载
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
Retinanet-Pytorch
Retinanet目标检测算法(简单,明了,易用,全中文注释,单机多卡训练,视频检测)(based on pytorch,Simple, Clear, Mutil GPU)
Statistical-Learning-Method_Code
手写实现李航《统计学习方法》书中全部算法
pytorch-retinanet
Pytorch implementation of RetinaNet object detection.
real-time-network
real-time network architecture for mobile devices and semantic segmentation
a-PyTorch-Tutorial-to-Object-Detection
SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
caltech-pedestrian-dataset-to-yolo-format-converter
converts the format of the caltech pedestrian dataset to the format that yolo uses
awesome-deep-learning-papers
The most cited deep learning papers
Accelerating-CNN-with-FPGA
This project accelerates CNN computation with the help of FPGA, for more than 50x speed-up compared with CPU.
Tiny_YOLO_v3_ZYNQ
Implement Tiny YOLO v3 on ZYNQ
High-Level-Synthesis-Flow-on-Zynq-using-Vivado-HLS
This course provides professors with an understanding of high-level synthesis design methodologies necessary to develop digital systems using Vivado HLS. Now under 2018.2 version.
2019_SummerCamp
2019 SEU-Xilinx Summer School