Giter VIP home page Giter VIP logo

sharingan's Introduction

SharinGAN

Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

The official project website for this work can be found here.

Requirements

Python 2.7 Pytorch 0.4.1

The trained model files for both the tasks are made available here here. The pretrained_models folder contains the pretrained model for the generator and the primary task networks before end-to-end training the SharinGAN network as a whole. The final trained models are present in Face_Normal_Estimation/ and Monocular_Depth_Estimation/ directories of the google drive.

The environment.yml file is also provided for one to replicate the environment.

We added the training and validation codes for both the tasks of Monocular Depth Estimation and Face Normal Estimation. We hope to improve the repository with time. We appreciate your inputs and feedback

Monocular Depth Estimation

Place the saved model file (Depth_Estimator_WI_geom_bicubic_da-144999.pth.tar) inside a newly created folder Monocular_Depth_Estimation/saved_models/ of the current repo.

The dataset files required for the dataloaders Kitti_dataloader.py and VKitti_dataloader.py are made available at Monocular_Depth_Estimation/dataset_files/.

Place the Monocular_Depth_Estimation/dataset_files/Kitti/.txt files in the original downloaded kitti/ dataset folder. Similarly place the Monocular_Depth_Estimation/dataset_files/VKitti/.txt files in the original downloaded Virtual_Kitti/ dataset folder.

Make3D evaluation

cd Monocular_Depth_Estimation
python Make3D_validation.py --iter 144999

sharingan's People

Contributors

koutilya-pnvr avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

sharingan's Issues

about the Kitti and vkitti dataset

Thank you very much for your excellent job and I think it is interesting. I have some trouble about the datasets, although I have download the Kitti and vkitty. Can you give me some help about how to prepare the datasets ?

More details are expected

Thanks for your contribution to this topic. Could you provide more details over your code. For example, the version of Pytorch, or a basic sample run using default parameters.

Looking for trained SharinGAN depth estimation model [KITTI]

Hi Koutilya,

Thanks for your contribution to this topic.

With the current script published in Git repo, we notice several bugs, and majorly for reproducing the paper results the important details are missing. [as mentioned in #2 also]

Can you please update us when the trained Sharingan models and/or the bug-free scripts will be available?

Thank you,

Comparing Different models

Hi,
Could you please tell me how you compared different models? Did you use the same learning rate, number of epochs, Number of decay epochs, image size, optimizer among all models? Also, did you collect test results using the final saved generator or did you use the best results testing all saved generators at different epochs?

Questions on the training time

Thanks for the great work!

May I ask what is the estimated training time for each stage, i.e., pretraining G, pretraining T, and jointly training G and T, on a single GPU like TITAN or V100? Thanks! : )

About Kitti data

Hi,
I'm struggling with the Kitti data u used in ur project, can u plz tell me which item did u download from the official Kitti website?
Maybe the data u used is Kitti 360? But I didn't find data which is labeled as 2011.
Thank you so much for ur help.

Code

Hi ! When can I download the codes of SharinGAN?

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.