jaguarzhang

mathematician，data analyst

Shanghai，P.R.China

Pinned Repositories

500LineorLess_CN
500 line or less 中文翻译计划。
Language:HTML00
adversarial-frcnn
A-Fast-RCNN (CVPR 2017)
Language:Python0 0 00
albumentations
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Language:Python0 0 00
altair
Declarative statistical visualization library for Python
Language:Python0 0 00
ATSS
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection, CVPR, Oral, 2020
Language:Python00
Augmentor
Image augmentation library in Python for machine learning.
Language:Jupyter Notebook00
automl
Google Brain AutoML
Language:Jupyter Notebook0 0 00
Awesome-Chinese-NLP
A curated list of resources for Chinese NLP 中文自然语言处理相关资料
0 0 00
bokeh
Interactive Data Visualization in the browser, from Python
Language:Python00
darknet
darknet深度学习框架源码分析：详细中文注释，涵盖框架原理与实现语法分析
Language:C00

jaguarzhang's Repositories

jaguarzhang/albumentations
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Language:Python0 0 00
jaguarzhang/altair
Declarative statistical visualization library for Python
Language:Python0 0 00
jaguarzhang/ATSS
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection, CVPR, Oral, 2020
Language:Python00
jaguarzhang/Augmentor
Image augmentation library in Python for machine learning.
Language:Jupyter Notebook00
jaguarzhang/automl
Google Brain AutoML
Language:Jupyter Notebook0 0 00
jaguarzhang/Awesome-Chinese-NLP
A curated list of resources for Chinese NLP 中文自然语言处理相关资料
0 0 00
jaguarzhang/bokeh
Interactive Data Visualization in the browser, from Python
Language:Python00
jaguarzhang/darknet-1
YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )
jaguarzhang/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
jaguarzhang/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
jaguarzhang/EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
jaguarzhang/fastNLP
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
jaguarzhang/FCOS
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
jaguarzhang/garbage_classify
最严垃圾分类政策自7月1日颁布，如何进行垃圾分类已经成为居民生活的灵魂拷问。但是，没关系！AI在垃圾分类的应用可以成为居民的得力助手。本次垃圾分类挑战杯，目的在于构建基于深度学习技术的图像分类模型，实现垃圾图片类别的精准识别，大赛参考深圳垃圾分类标准，按可回收物、厨余垃圾、有害垃圾和其他垃圾四项分类。
jaguarzhang/imgaug
Image augmentation for machine learning experiments.
jaguarzhang/KL-Loss
Bounding Box Regression with Uncertainty for Accurate Object Detection (CVPR'19)
jaguarzhang/mmdetection
OpenMMLab Detection Toolbox and Benchmark
jaguarzhang/models
Models and examples built with TensorFlow
jaguarzhang/OpenCV-Python-Tutorials
OpenCV+Python Tutorials
jaguarzhang/practical-python
Practical Python Programming (course by @dabeaz)
jaguarzhang/PSConv
[ECCV 2020] PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
jaguarzhang/Python-1
All Algorithms implemented in Python
jaguarzhang/PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
jaguarzhang/pytorch-YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4
jaguarzhang/PyTorch_YOLOv4
PyTorch implementation of YOLOv4
jaguarzhang/Statistical-Learning-Method_Code
手写实现李航《统计学习方法》书中全部算法
jaguarzhang/tpu
Reference models and tools for Cloud TPUs.
jaguarzhang/vision
Datasets, Transforms and Models specific to Computer Vision
jaguarzhang/Yet-Another-EfficientDet-Pytorch
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
jaguarzhang/YOLOv3_TensorFlow
Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.