yanghedada

Pinned Repositories

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华为人口年龄预测
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--JDD
京东高潜用户购买预测---季军
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2018--ZJUAI--PyramidBoxDetector
2018 云从人头技术冠军分享方案
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2018-CCF-BDCI--TOP3
参赛者需要根据给出的基金净值、基金业绩比较基准、对应指数行情、基金间相关性等数据，构建模型、算法进行训练。
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2018-CCF-BDCI-China-Unicom-Research-Institute-top2
2018-CCF大数据与计算智能大赛-面向电信行业存量用户的智能套餐个性化匹配模型联通赛-复赛第二名解决方案
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2018-CCL-UIIMCS
CCL2018中移在线客服领域用户意图分类冠军1st方案
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2018-daguan-competition-rank4
2018年"达观杯"文本智能处理挑战赛-长文本分类：季军 (4st/3131)
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2018-DC-DataGrand-TextIntelProcess
2018-DC-“达观杯”文本智能处理挑战赛：冠军 (1st/3131)
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pruning
CNN‘s model compression and pruning
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yang
start tests
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yanghedada's Repositories

yanghedada/2019_algorithm_intern_information
2020年的算法实习岗位信息表，部分包括内推码，和常见深度学习算法岗面试题及答案，暑期计算机视觉实习面经和总结
yanghedada/awesome-semantic-segmentation-pytorch
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
Language:Python1 0
yanghedada/Awesome-Super-Resolution
Collect super-resolution related papers, data, repositories
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yanghedada/books-recommendation
程序员进阶、面试书籍（视频），持续更新（Programmer Books）
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yanghedada/books_list_backup_2018_2019
book lab
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yanghedada/cnn-explainer
Learning Convolutional Neural Networks with Interactive Visualization. https://poloclub.github.io/cnn-explainer/
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yanghedada/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
code and model of Pyramid Feature Selective Network for Saliency detection
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yanghedada/Deep-Learning-Interview-Book
深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）
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yanghedada/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
1 01
yanghedada/DigixContest
用户人口属性预测竞赛
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yanghedada/faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
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yanghedada/FSRNET_pytorch
Dear Friend, Having tried several ways, I tried to reproduce the performance of FSRNet by using Pytorch. I am Now transferring to another new project FSRNet, You can download the code and run at your own machine. Feel free to contact me.
Language:Python1 0
yanghedada/ganhacks
starter from "How to Train a GAN?" at NIPS2016
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yanghedada/huawei_Algorithm_contest
华为算法大赛香港boost、
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yanghedada/Improved-YOLOv3-for-UAV
在无人机视角数据集基础上，使用改进的YOLOv3模型进行人物检测精度和准确度提升
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yanghedada/JavaGuide
【Java学习+面试指南】一份涵盖大部分Java程序员所需要掌握的核心知识。
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yanghedada/Knowledge-Distillation-Zoo
Pytorch implementation of various Knowledge Distillation (KD) methods.
Language:Python1 0
yanghedada/model-compression
model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)
Language:Python1 0
yanghedada/monoloco
[ICCV 2019] Official implementation of "MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation" in PyTorch + Social Distancing
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yanghedada/NAS-DIP-pytorch
[ECCV 2020] NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
Language:Python1 0
yanghedada/Object-detection-using-yolov2-and-distance-estimation
Object detection using yolov2 and estimation of object from the camera lens
yanghedada/object_specific_dist_from_monucular_image_iccv19
Open Source Implementation of the paper "Learning Object Specific Distance From a Monocular Image" published in ICCV 2019
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yanghedada/ProtoPNet
This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).
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yanghedada/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
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yanghedada/real-world-sr
[ICCVW 2019] PyTorch implementation of DSGAN and ESRGAN-FS from the paper "Frequency Separation for Real-World Super-Resolution". This code was the winning solution of the AIM challenge on Real-World Super-Resolution at ICCV 2019
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yanghedada/RoIAlign.pytorch
RoIAlign & crop_and_resize for PyTorch
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yanghedada/TTSR
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution
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yanghedada/vega
AutoML tools chain
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yanghedada/VideoSuperResolution
A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.
Language:Python1 0
yanghedada/Yet-Another-EfficientDet-Pytorch
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.