Pinned Repositories
A-to-Z-Resources-for-Students
Curated list of resources for college students
adversarial-frcnn-master
基于GAN的目标检测
caffe
Caffe: a fast open framework for deep learning.
caffe-windows
Configure Caffe in one hour for Windows users.
chatbot-base-on-Knowledge-Graph
使用深度学习方法解析问题 知识图谱存储 查询知识点 基于医疗垂直领域的对话系统
ChineseCodingInterviewAppendix
The source code for the appendix part of the Chinese version of the book Coding Interviews
classify
animal classify
cv-papers
计算机视觉相关论文整理、记录、分享; 包括图像分类、目标检测、视觉跟踪/目标跟踪、人脸识别/人脸验证、OCR/场景文本检测及识别等领域。欢迎加星,欢迎指正错误,同时也期待能够共同参与！！! 持续更新中... ...
CV_Homework
typhoon_prediction
# typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA. ## Requirements * BeautifulSoup * PIL * Pytorch ## Usage 1. Run `download.py`, download the satellite images of typhoons to folder `tys_raw`. 2. Run `create_samples.py`, convert raw data into the legal samples for our CNN, create two new forlder `train_set` and `test_set`. 3. Train CNN using `train_net.py`, the trained CNN will be saved as a disk file `net_relu.pt`. 4. Run `test_net.py`, analysis the test set. After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set.  Here is what this CNN thinks of the top 20 typhoons sorted by max wind. ``` 1 ('197920', 130.27679443359375) 2 ('200914', 127.7662582397461) 3 ('199019', 122.92172241210938) 4 ('200918', 122.84004211425781) 5 ('201614', 122.66597747802734) 6 ('201601', 122.03250885009766) 7 ('201513', 121.75947570800781) 8 ('200922', 121.35771942138672) 9 ('201013', 120.0194091796875) 10 ('201330', 118.92587280273438) 11 ('201419', 117.6025390625) 12 ('198305', 117.10270690917969) 13 ('201422', 116.77259063720703) 14 ('198522', 116.46116638183594) 15 ('201327', 116.42304992675781) 16 ('201216', 116.36921691894531) 17 ('198221', 116.18096923828125) 18 ('199230', 115.96656799316406) 19 ('198210', 115.96611022949219) 20 ('201328', 115.57132720947266) ``` ## Tips * Memory should be at least 1.5G . * This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training. * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).
yty3805595's Repositories
yty3805595/typhoon_prediction
# typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA. ## Requirements * BeautifulSoup * PIL * Pytorch ## Usage 1. Run `download.py`, download the satellite images of typhoons to folder `tys_raw`. 2. Run `create_samples.py`, convert raw data into the legal samples for our CNN, create two new forlder `train_set` and `test_set`. 3. Train CNN using `train_net.py`, the trained CNN will be saved as a disk file `net_relu.pt`. 4. Run `test_net.py`, analysis the test set. After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set.  Here is what this CNN thinks of the top 20 typhoons sorted by max wind. ``` 1 ('197920', 130.27679443359375) 2 ('200914', 127.7662582397461) 3 ('199019', 122.92172241210938) 4 ('200918', 122.84004211425781) 5 ('201614', 122.66597747802734) 6 ('201601', 122.03250885009766) 7 ('201513', 121.75947570800781) 8 ('200922', 121.35771942138672) 9 ('201013', 120.0194091796875) 10 ('201330', 118.92587280273438) 11 ('201419', 117.6025390625) 12 ('198305', 117.10270690917969) 13 ('201422', 116.77259063720703) 14 ('198522', 116.46116638183594) 15 ('201327', 116.42304992675781) 16 ('201216', 116.36921691894531) 17 ('198221', 116.18096923828125) 18 ('199230', 115.96656799316406) 19 ('198210', 115.96611022949219) 20 ('201328', 115.57132720947266) ``` ## Tips * Memory should be at least 1.5G . * This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training. * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).
yty3805595/A-to-Z-Resources-for-Students
Curated list of resources for college students
yty3805595/caffe
Caffe: a fast open framework for deep learning.
yty3805595/chatbot-base-on-Knowledge-Graph
使用深度学习方法解析问题 知识图谱存储 查询知识点 基于医疗垂直领域的对话系统
yty3805595/classify
animal classify
yty3805595/cv-papers
计算机视觉相关论文整理、记录、分享; 包括图像分类、目标检测、视觉跟踪/目标跟踪、人脸识别/人脸验证、OCR/场景文本检测及识别等领域。欢迎加星,欢迎指正错误,同时也期待能够共同参与！！! 持续更新中... ...
yty3805595/CV_Homework
yty3805595/Dection-of-object-in-video
YOLO
yty3805595/Deep-Learning-With-Python
《Python深度学习》书籍代码
yty3805595/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
yty3805595/face_detection
人脸检测 目标点检测
yty3805595/fucking-algorithm
手把手撕LeetCode题目，扒各种算法套路的裤子。English version supported! Crack LeetCode, not only how, but also why.
yty3805595/git_rep
week5
yty3805595/GZHU-Report-Latex-Version
我自己制作的广州大学Latex报告模板，有毕业设计，课程设计，毕业论文，等等🎈
yty3805595/homwork
yty3805595/HyperDL-Tutorial
深度学习教程整理 | 干货
yty3805595/interview_internal_reference
2019年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。
yty3805595/jupyters_and_slides
yty3805595/langchain
⚡ Building applications with LLMs through composability ⚡
yty3805595/learn-regex
Learn regex the easy way
yty3805595/nlp_paper_study
该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记
yty3805595/ParlAI
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
yty3805595/Python
All Algorithms implemented in Python
yty3805595/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
yty3805595/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
yty3805595/References
yty3805595/simple-faster-rcnn-pytorch
A simplified implemention of Faster R-CNN that replicate performance from origin paper
yty3805595/stock_opinion_anlysis
yty3805595/team-learning
Datawhale组队学习计划与课程内容
yty3805595/yty3805595
Config files for my GitHub profile.