Code Monkey home page Code Monkey logo

greak-1124's Projects

ai_tutorial icon ai_tutorial

精选机器学习，NLP，图像识别， 深度学习等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理。算法大牛笔记汇总

dabnet icon dabnet

Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation (BMVC2019)

gradcam-and-gradcamplus_plus icon gradcam-and-gradcamplus_plus

Based on pytoch, gradcam and gradcam + + are encapsulated into easy-to-use API, and some interesting tests are done with pre trained vgg16, alexnet, densenet 121, mobilenet, resnet18, squeezene. Interested friends can give a star, thank you.

jiaozicup_keras_788_baseline icon jiaozicup_keras_788_baseline

2020年“创青春·交子杯”新网银行金融科技挑战赛-AI算法赛道keras版本baseline，线上成绩788。

kaggle_m5_forecasting_accuracy_pro icon kaggle_m5_forecasting_accuracy_pro

This is a competition M5 forecasting - accuracy on kaggle. For details, please refer to the link: https://www.kaggle.com/c/m5-forecasting-accuracy/overview. This is my first time to take part in the kaggle competition. After two months of hard work, I finally ranked 172, top4% and won a silver medal.

lmffnet icon lmffnet

Real-time semantic segmentation is widely used in the field of autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation tasks. In this paper, we introduce a Lightweight-Multiscale-Feature-Fusion Network (LMFFNet) mainly composed of three types of components: Split-Extract-Merge Bottleneck (SEM-B) block, Features Fusion Module (FFM), and Multiscale Attention Decoder (MAD). The SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy. The MAD well recovers the details of the input images through the attention mechanism. Two networks combined with different components are proposed based on the LMFFNet model. Without pretraining, the smaller network of LMFFNet-S achieves 72.7% mIoU on Cityscapes test set at the 512×1024 resolution with only 1.1 M parameters at a reference speed of 98.9 fps running on a GTX1080Ti GPU while the larger version of LMFFNet-L achieves 74.7% mIoU with 1.4 M parameters at 89.6 fps. Besides, 67.7% mIoU at 208.9 fps and 70.3% mIoU at 72.4 fps are respectively achieved for 360 × 480 and 720 × 960 resolutions on CamVid test set using LMFFNet-S while LMFFNet--L achieves 68.1% mIoU at 182.9 fps and 71.0% mIoU at 66.5 fps, correspondingly. The proposed LMFFNets make an adequate trade-off between accuracy and parameter size for real-time inference for semantic segmentation tasks.

yolov5_ncnn icon yolov5_ncnn

🍅 Deploy ncnn on mobile phones. Support Android and iOS. 移动端ncnn部署，支持Android与iOS。

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.