RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Overview

RIFE

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Ported from https://github.com/hzwer/arXiv2020-RIFE

Dependencies

  • NumPy
  • PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
  • VapourSynth

Installation

pip install --upgrade vsrife

Usage

from vsrife import RIFE

ret = RIFE(clip)

See __init__.py for the description of the parameters.

Comments
  • Getting Error when interpolating

    Getting Error when interpolating

        model.load_model(os.path.join(os.path.dirname(__file__), model_dir), -1)
      File "C:\Users\\AppData\Local\Programs\Python\Python39\lib\site-packages\vsrife\RIFE_HDv2.py", line 164, in load_model
        convert(torch.load('{}/flownet.pkl'.format(path), map_location=self.torch_device)))
      File "C:\Users\\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\serialization.py", line 608, in load
        return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
      File "C:\Users\\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\serialization.py", line 777, in _legacy_load
        magic_number = pickle_module.load(f, **pickle_load_args)
    EOFError: Ran out of input  ```
    
    Source file is a 720p 30fps mp4, loaded into VS through Lsmash source, set the format to RGBS. Nothing else
    System specs are R7 3700x, 32GB of ram and a RTX 3060
    
    
    opened by banjaminicc 4
  • Small feature request for RIFEv4: target fps as alternative to multiplier

    Small feature request for RIFEv4: target fps as alternative to multiplier

    I would it be possible to allow setting a target fps instead of a multiplier when using RIFEv4? When going from for example 23.976 (24000/1001) to 60 fps, having to use (60 * 1001 / 24000 =) 2,5025 is kind of annoying. ;) I know could write a wrapper arount the rife.RIFE but I suspect depending on the resulting float it would be more accurate if this was done inside the filter.

    opened by Selur 3
  • vs-rife + latest vs-dpir don't work

    vs-rife + latest vs-dpir don't work

    When using just vs-rife:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    # Loading Plugins
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/MiscFilter/MiscFilters/MiscFilters.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DeinterlaceFilter/TIVTC/libtivtc.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/d2vSource/d2vsource.dll")
    # source: 'C:\Users\Selur\Desktop\VTS_01_1.VOB'
    # current color space: YUV420P8, bit depth: 8, resolution: 720x480, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: telecine
    # Loading C:\Users\Selur\Desktop\VTS_01_1.VOB using D2VSource
    clip = core.d2v.Source(input="E:/Temp/vob_941fdaaeda22090766694391cc4281d5_853323747.d2v")
    # Setting color matrix to 470bg.
    clip = core.std.SetFrameProps(clip, _Matrix=5)
    clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
    clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
    # Setting color range to TV (limited) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
    # making sure frame rate is set to 29.970
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Deinterlacing using TIVTC
    clip = core.tivtc.TFM(clip=clip)
    clip = core.tivtc.TDecimate(clip=clip, mode=7, rate=10, dupThresh=0.04, vidThresh=3.50, sceneThresh=15.00)# new fps: 10
    # make sure content is preceived as frame based
    clip = core.std.SetFieldBased(clip, 0)
    clip = core.misc.SCDetect(clip=clip,threshold=0.150)
    from vsrife import RIFE
    # adjusting color space from YUV420P8 to RGBS for VsTorchRIFE
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
    # adjusting frame count&rate with RIFE (torch)
    clip = RIFE(clip, multi=3, device_type='cuda', device_index=0) # new fps: 20
    # adjusting output color from: RGBS to YUV420P8 for x264Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P8, matrix_s="470bg", range_s="limited")
    # set output frame rate to 30.000fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30, fpsden=1)
    # Output
    clip.set_output()
    

    everything works. But when I add latest vs-dpir:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    import os
    import site
    # Import libraries for onnxruntime
    from ctypes import WinDLL
    path = site.getsitepackages()[0]+'/onnxruntime_dlls/'
    WinDLL(path+'cublas64_11.dll')
    WinDLL(path+'cudart64_110.dll')
    WinDLL(path+'cudnn64_8.dll')
    WinDLL(path+'cudnn_cnn_infer64_8.dll')
    WinDLL(path+'cudnn_ops_infer64_8.dll')
    WinDLL(path+'cufft64_10.dll')
    WinDLL(path+'cufftw64_10.dll')
    WinDLL(path+'nvinfer.dll')
    WinDLL(path+'nvinfer_plugin.dll')
    WinDLL(path+'nvparsers.dll')
    WinDLL(path+'nvonnxparser.dll')
    # Loading Plugins
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/MiscFilter/MiscFilters/MiscFilters.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DeinterlaceFilter/TIVTC/libtivtc.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/d2vSource/d2vsource.dll")
    # source: 'C:\Users\Selur\Desktop\VTS_01_1.VOB'
    # current color space: YUV420P8, bit depth: 8, resolution: 720x480, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: telecine
    # Loading C:\Users\Selur\Desktop\VTS_01_1.VOB using D2VSource
    clip = core.d2v.Source(input="E:/Temp/vob_941fdaaeda22090766694391cc4281d5_853323747.d2v")
    # Setting color matrix to 470bg.
    clip = core.std.SetFrameProps(clip, _Matrix=5)
    clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
    clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
    # Setting color range to TV (limited) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
    # making sure frame rate is set to 29.970
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Deinterlacing using TIVTC
    clip = core.tivtc.TFM(clip=clip)
    clip = core.tivtc.TDecimate(clip=clip, mode=7, rate=10, dupThresh=0.04, vidThresh=3.50, sceneThresh=15.00)# new fps: 10
    # make sure content is preceived as frame based
    clip = core.std.SetFieldBased(clip, 0)
    from vsdpir import DPIR
    # adjusting color space from YUV420P8 to RGBS for vsDPIRDenoise
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
    # denoising using DPIRDenoise
    clip = DPIR(clip=clip, strength=15.000, task="denoise", provider=1, device_id=0)
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV444P16, matrix_s="470bg", range_s="limited")
    clip = core.misc.SCDetect(clip=clip,threshold=0.150)
    from vsrife import RIFE
    # adjusting color space from YUV444P16 to RGBS for VsTorchRIFE
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
    # adjusting frame count&rate with RIFE (torch)
    clip = RIFE(clip, multi=3, device_type='cuda', device_index=0) # new fps: 20
    # adjusting output color from: RGBS to YUV420P8 for x264Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P8, matrix_s="470bg", range_s="limited")
    # set output frame rate to 30.000fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30, fpsden=1)
    # Output
    clip.set_output()
    

    I get:

    Python exception: [WinError 127] Die angegebene Prozedur wurde nicht gefunden. Error loading "I:\Hybrid\64bit\Vapoursynth\Lib/site-packages\torch\lib\cudnn_cnn_train64_8.dll" or one of its dependencies.
    

    Using just vs-dpir:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    import os
    import site
    # Import libraries for onnxruntime
    from ctypes import WinDLL
    path = site.getsitepackages()[0]+'/onnxruntime_dlls/'
    WinDLL(path+'cublas64_11.dll')
    WinDLL(path+'cudart64_110.dll')
    WinDLL(path+'cudnn64_8.dll')
    WinDLL(path+'cudnn_cnn_infer64_8.dll')
    WinDLL(path+'cudnn_ops_infer64_8.dll')
    WinDLL(path+'cufft64_10.dll')
    WinDLL(path+'cufftw64_10.dll')
    WinDLL(path+'nvinfer.dll')
    WinDLL(path+'nvinfer_plugin.dll')
    WinDLL(path+'nvparsers.dll')
    WinDLL(path+'nvonnxparser.dll')
    # Loading Plugins
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/DeinterlaceFilter/TIVTC/libtivtc.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/d2vSource/d2vsource.dll")
    # source: 'C:\Users\Selur\Desktop\VTS_01_1.VOB'
    # current color space: YUV420P8, bit depth: 8, resolution: 720x480, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: telecine
    # Loading C:\Users\Selur\Desktop\VTS_01_1.VOB using D2VSource
    clip = core.d2v.Source(input="E:/Temp/vob_941fdaaeda22090766694391cc4281d5_853323747.d2v")
    # Setting color matrix to 470bg.
    clip = core.std.SetFrameProps(clip, _Matrix=5)
    clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=5)
    clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=5)
    # Setting color range to TV (limited) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
    # making sure frame rate is set to 29.970
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Deinterlacing using TIVTC
    clip = core.tivtc.TFM(clip=clip)
    clip = core.tivtc.TDecimate(clip=clip, mode=7, rate=10, dupThresh=0.04, vidThresh=3.50, sceneThresh=15.00)# new fps: 10
    # make sure content is preceived as frame based
    clip = core.std.SetFieldBased(clip, 0)
    from vsdpir import DPIR
    # adjusting color space from YUV420P8 to RGBS for vsDPIRDenoise
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
    # denoising using DPIRDenoise
    clip = DPIR(clip=clip, strength=15.000, task="denoise", provider=1, device_id=0)
    # adjusting output color from: RGBS to YUV420P8 for x264Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P8, matrix_s="470bg", range_s="limited")
    # set output frame rate to 10.000fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=10, fpsden=1)
    # Output
    clip.set_output()
    

    works fine.

    -> do you have an idea how I could fix this?

    opened by Selur 3
  • half the image is broken when using 4k content

    half the image is broken when using 4k content

    I get a broken output (see attachment), when using:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    # Loading Plugins
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/MiscFilter/MiscFilters/MiscFilters.dll")
    core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
    # source: 'G:\TestClips&Co\files\MPEG-4 H.264\4k\Back to the Future (1985) 4k 10bit - 0.10.35-0.11.35.mkv'
    # current color space: YUV420P10, bit depth: 10, resolution: 3840x2076, fps: 23.976, color matrix: 2020ncl, yuv luminance scale: limited, scanorder: progressive
    # Loading G:\TestClips&Co\files\MPEG-4 H.264\4k\Back to the Future (1985) 4k 10bit - 0.10.35-0.11.35.mkv using LWLibavSource
    clip = core.lsmas.LWLibavSource(source="G:/TestClips&Co/files/MPEG-4 H.264/4k/Back to the Future (1985) 4k 10bit - 0.10.35-0.11.35.mkv", format="YUV420P10", cache=0, fpsnum=24000, fpsden=1001, prefer_hw=1)
    # Setting color matrix to 2020ncl.
    clip = core.std.SetFrameProps(clip, _Matrix=9)
    clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=9)
    clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=9)
    # Setting color range to TV (limited) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
    # making sure frame rate is set to 23.976
    clip = core.std.AssumeFPS(clip=clip, fpsnum=24000, fpsden=1001)
    clip = core.misc.SCDetect(clip=clip,threshold=0.150)
    from vsrife import RIFE
    # adjusting color space from YUV420P10 to RGBS for VsTorchRIFE
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="2020ncl", range_s="limited")
    # adjusting frame count&rate with RIFE (torch)
    clip = RIFE(clip, scale=0.5, multi=3, device_type='cuda', device_index=0, fp16=True) # new fps: 71.928
    # adjusting output color from: RGBS to YUV420P8 for x264Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P8, matrix_s="2020ncl", range_s="limited", dither_type="error_diffusion")
    # set output frame rate to 71.928fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=8991, fpsden=125)
    # Output
    clip.set_output()
    

    tried different scale values, fp16 disabled, without scene change detection and other values for mult, nothing helped. https://github.com/HomeOfVapourSynthEvolution/VapourSynth-RIFE-ncnn-Vulkan works fine. 2k content also works fine. I tried different source filters and different files. Would be nice if this could be fixed.

    attachment was too large: https://ibb.co/WGT9pvL

    opened by Selur 2
  • Vapoursynth R58 and Python 3.10 compatibilty

    Vapoursynth R58 and Python 3.10 compatibilty

    trying to install vs-rife in Vapoursynth R58 I get:

    I:\Hybrid\64bit\Vapoursynth>python -m pip install --upgrade vsrife
    Collecting vsrife
      Using cached vsrife-2.0.0-py3-none-any.whl (32.5 MB)
    Requirement already satisfied: torch>=1.9.0 in i:\hybrid\64bit\vapoursynth\lib\site-packages (from vsrife) (1.11.0+cu113)
    Requirement already satisfied: numpy in i:\hybrid\64bit\vapoursynth\lib\site-packages (from vsrife) (1.22.3)
    Collecting VapourSynth>=55
      Using cached VapourSynth-57.zip (567 kB)
      Preparing metadata (setup.py) ... error
      error: subprocess-exited-with-error
    
      × python setup.py egg_info did not run successfully.
      │ exit code: 1
      ╰─> [15 lines of output]
          Traceback (most recent call last):
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-s7976394\vapoursynth_701a37362cd045f58da4818d07217c99\setup.py", line 64, in <module>
              dll_path = query(winreg.HKEY_LOCAL_MACHINE, REGISTRY_PATH, REGISTRY_KEY)
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-s7976394\vapoursynth_701a37362cd045f58da4818d07217c99\setup.py", line 38, in query
              reg_key = winreg.OpenKey(hkey, path, 0, winreg.KEY_READ)
          FileNotFoundError: [WinError 2] Das System kann die angegebene Datei nicht finden
    
          During handling of the above exception, another exception occurred:
    
          Traceback (most recent call last):
            File "<string>", line 2, in <module>
            File "<pip-setuptools-caller>", line 34, in <module>
            File "C:\Users\Selur\AppData\Local\Temp\pip-install-s7976394\vapoursynth_701a37362cd045f58da4818d07217c99\setup.py", line 67, in <module>
              raise OSError("Couldn't detect vapoursynth installation path")
          OSError: Couldn't detect vapoursynth installation path
          [end of output]
    
      note: This error originates from a subprocess, and is likely not a problem with pip.
    error: metadata-generation-failed
    
    × Encountered error while generating package metadata.
    ╰─> See above for output.
    
    note: This is an issue with the package mentioned above, not pip.
    hint: See above for details.
    

    any idea how to fix it?

    opened by Selur 2
  • How to set 'clip.num_frames

    How to set 'clip.num_frames

    How to set the frames numbers?I only found the "multi: int ="in "init.py".Can I set the whole number of the frames numbers?Like 60 fps?Thanks!

    opened by feaonal 2
  • Requesting example vapoursynth script

    Requesting example vapoursynth script

    I tried to create a valid script for a while, but I can't make it run.

    from vsrife import RIFE
    import vapoursynth as vs
    core = vs.core
    core.std.LoadPlugin(path='/usr/lib/x86_64-linux-gnu/libffms2.so')
    clip = core.ffms2.Source(source='test.webm')
    print(clip) # YUV420P8
    clip = vs.core.resize.Bicubic(clip, format=vs.RGBS)
    print(clip) # RGBS
    clip = RIFE(clip)
    clip.set_output()
    
    vspipe --y4m inference.py - | x264 - --demuxer y4m -o example.mkv
    
    Error: Failed to retrieve frame 0 with error: Resize error: Resize error 3074: no path between colorspaces (2/2/2 => 0/2/2). May need to specify additional colorspace parameters.
    

    Can I get an example that should actually work?

    opened by styler00dollar 2
  • [Q] 0bit models in the repo

    [Q] 0bit models in the repo

    Hi

    i see in the model folders, have a files (models?) with 0bits, i presume when the plugin "learn", the models is filled with the data

    this is correct?

    then, in a system with install this plugin as system-wide, these models should be have a write permissions? (in case of linux)

    greetings

    opened by sl1pkn07 2
  • Wrong output framerate

    Wrong output framerate

    That - https://github.com/HolyWu/vs-rife/blob/91e894f41cbdfb458ef8f776c47c7f652158bc6f/vsrife/init.py#L280 - doesn't work as expected because of two reasons:

    1. clip.fps.numerator / denominator can be 0 / 1 (from the docs: "It is 0/1 when the clip has a variable framerate")
    2. there's a frame duration attached to each frame, and it seems like FrameEval(frame_adjuster) return frames with the original durations, not the ones from format_clip

    A quick fix that works:

        clip0 = vs.core.std.Interleave([clip] * factor_num)
        if factor_den>1:
            clip0 = clip0.std.SelectEvery(cycle=factor_den,offsets=0)
        clip1 = clip.std.DuplicateFrames(frames=clip.num_frames - 1).std.DeleteFrames(frames=0)
        clip1 = vs.core.std.Interleave([clip1] * factor_num)
        if factor_den>1:
            clip1 = clip1.std.SelectEvery(cycle=factor_den,offsets=0)
    
    opened by chainikdn 1
  • How to set clip.num_frames

    How to set clip.num_frames

    How to set the frames numbers?I only found the "multi: int ="in "init.py".Can I set the whole number of the frames numbers?Like 60 fps?Thanks!

    opened by feaonal 0
Releases(v3.1.0)
[NeurIPS'20] Multiscale Deep Equilibrium Models

Multiscale Deep Equilibrium Models 💥 💥 💥 💥 This repo is deprecated and we will soon stop actively maintaining it, as a more up-to-date (and simple

CMU Locus Lab 221 Dec 26, 2022
Using image super resolution models with vapoursynth and speeding them up with TensorRT

vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since

4 Aug 23, 2022
An unopinionated replacement for PyTorch's Dataset and ImageFolder, that handles Tar archives

Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just

Joao Henriques 47 Dec 20, 2022
Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Accelerating Reinforcement Learning with Learned Skill Priors [Project Website] [Paper] Karl Pertsch1, Youngwoon Lee1, Joseph Lim1 1CLVR Lab, Universi

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 134 Dec 06, 2022
Global-Local Context Network for Person Search

Global-Local Context Network for Person Search Abstract: Person search aims to jointly localize and identify a query person from natural, uncropped im

Peng Zheng 15 Oct 17, 2022
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J

4 Sep 23, 2022
Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

Knowledge Distillation for BERT Unsupervised Domain Adaptation Official PyTorch implementation | Paper Abstract A pre-trained language model, BERT, ha

Minho Ryu 29 Nov 30, 2022
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
Code & Data for Enhancing Photorealism Enhancement

Code & Data for Enhancing Photorealism Enhancement

Intel ISL (Intel Intelligent Systems Lab) 1.1k Jan 08, 2023
Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ

Taosha Fan 47 Nov 15, 2022
Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints". Edit 2021/

10 Dec 20, 2022
Efficient training of deep recommenders on cloud.

HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and

Alibaba 111 Dec 23, 2022
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

SERank An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow

Zhihu 44 Oct 20, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
Efficient 3D human pose estimation in video using 2D keypoint trajectories

3D human pose estimation in video with temporal convolutions and semi-supervised training This is the implementation of the approach described in the

Meta Research 3.1k Dec 29, 2022
Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

Alireza Kiaeipour 7 Jul 10, 2022
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
Python library containing BART query generation and BERT-based Siamese models for neural retrieval.

Neural Retrieval Embedding-based Zero-shot Retrieval through Query Generation leverages query synthesis over large corpuses of unlabeled text (such as

Amazon Web Services - Labs 35 Apr 14, 2022