Displaying 1 to 16 from 16 results

unet - unet for image segmentation

  •    Jupyter

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation. The original dataset is from isbi challenge, and I've downloaded it and done the pre-processing.

PaddleSeg - End-to-end image segmentation kit based on PaddlePaddle.

  •    Python

Welcome to PaddleSeg! PaddleSeg is an end-to-end image segmentation development kit developed based on PaddlePaddle, which covers a large number of high-quality segmentation models in different directions such as high-performance and lightweight. With the help of modular design, we provide two application methods: Configuration Drive and API Calling. So one can conveniently complete the entire image segmentation application from training to deployment through configuration calls or API calls. High Performance Model: Based on the high-performance backbone trained by Baidu's self-developed semi-supervised label knowledge distillation scheme (SSLD), combined with the state of the art segmentation technology, we provides 50+ high-quality pre-training models, which are better than other open source implementations.

unet_keras - unet_keras use image Semantic segmentation

  •    Python

unet_keras use image Semantic segmentation




CarND-Capstone-Wolf-Pack - ROS-based code to control a real self-driving car

  •    Python

This is the Capstone project for the Udacity Self-Driving Car Nanodegree. We developed software to guide a real self-driving car around a test track. Using the Robot Operating System (ROS), we created nodes for traffic light detection and classification, trajectory planning, and control. Note: Find the latest version of this project on Github.

cntk_unet - CNTK implementation of U-Net for image segmentation

  •    Jupyter

This is a CNTK implementation of U-net, which is a deep learning segmentation method proposed by Ronneberger et al.


ds_bowl_2018 - Kaggle Data Science Bowl 2018

  •    Jupyter

This is a DWT-inspired solution to the Kaggle's 2018 DS Bowl I produced within approximately 1 week before the end of the compeititon. UPDATE 2018-04-22 - my score was 114th. I guess they are cleaning the LB in the end.

open-solution-data-science-bowl-2018 - Open solution to the Data Science Bowl 2018

  •    Python

This is an open solution to the Data Science Bowl 2018 based on the topcoders winning solution from ods.ai. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉.

open-solution-salt-identification - Open solution to the TGS Salt Identification Challenge https://www

  •    Jupyter

This is an open solution to the TGS Salt Identification Challenge. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.

open-solution-ship-detection - Open solution to the Airbus Ship Detection Challenge

  •    Python

This is an open solution to the Airbus Ship Detection Challenge. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.

u-net-brain-tumor - U-Net Brain Tumor Segmentation in TensorFlow

  •    Python

🚀:Sep 2018 the data processing implementation in this repo is not the fastest, please use TensorFlow dataset API instead. This repo show you how to train a U-Net for brain tumor segmentation. By default, you need to download the training set of BRATS 2017 dataset, which have 210 HGG and 75 LGG volumes, and put the data folder along with all scripts.

river_ice_segmentation - River ice segmentation with deep learning

  •    Jupyter

unet, tf_unet and image-segmentation-keras/deeplab_keras contain other implementations of these models that did not work as well as the above. The commands for running each model are provided in a .md file in the corresponding folder. For example, commands for UNet and DenseNet are in image-segmentation-keras/unet.md and densenet/densenet.md. The commands are organized hierarchically into categories of experiments and a table of contents is included for easier navigation.






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