Deep Learning for Time Series Classification

Overview

Deep Learning for Time Series Classification

This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv.

architecture resnet

Data

The data used in this project comes from two sources:

  • The UCR/UEA archive, which contains the 85 univariate time series datasets.
  • The MTS archive, which contains the 13 multivariate time series datasets.

Code

The code is divided as follows:

  • The main.py python file contains the necessary code to run an experiement.
  • The utils folder contains the necessary functions to read the datasets and visualize the plots.
  • The classifiers folder contains nine python files one for each deep neural network tested in our paper.

To run a model on one dataset you should issue the following command:

python3 main.py TSC Coffee fcn _itr_8

which means we are launching the fcn model on the univariate UCR archive for the Coffee dataset (see constants.py for a list of possible options).

Prerequisites

All python packages needed are listed in pip-requirements.txt file and can be installed simply using the pip command. The code now uses Tensorflow 2.0. The results in the paper were generated using the Tensorflow 1.14 implementation which can be found here. Using Tensorflow 2.0 should give the same results.
Now InceptionTime is included in the mix, feel free to send a pull request to add another classifier.

Results

I added the results on the 128 datasets from the UCR archive 2018. Our results in the paper showed that a deep residual network architecture performs best for the time series classification task.

The following table contains the averaged accuracy over 10 runs of each implemented model on the UCR/UEA archive, with the standard deviation between parentheses.

Datasets MLP FCN ResNet Encoder MCNN t-LeNet MCDCNN Time-CNN TWIESN
50words 68.4(7.1) 62.7(6.1) 74.0(1.5) 72.3(1.0) 22.0(24.3) 12.5(0.0) 58.9(5.3) 62.1(1.0) 49.6(2.6)
Adiac 39.7(1.9) 84.4(0.7) 82.9(0.6) 48.4(2.5) 2.2(0.6) 2.0(0.0) 61.0(8.7) 37.9(2.0) 41.6(4.5)
ArrowHead 77.8(1.2) 84.3(1.5) 84.5(1.2) 80.4(2.9) 33.9(4.7) 30.3(0.0) 68.5(6.7) 72.3(2.6) 65.9(9.4)
Beef 72.0(2.8) 69.7(4.0) 75.3(4.2) 64.3(5.0) 20.0(0.0) 20.0(0.0) 56.3(7.8) 76.3(1.1) 53.7(14.9)
BeetleFly 87.0(2.6) 86.0(9.7) 85.0(2.4) 74.5(7.6) 50.0(0.0) 50.0(0.0) 58.0(9.2) 89.0(3.2) 73.0(7.9)
BirdChicken 77.5(3.5) 95.5(3.7) 88.5(5.3) 66.5(5.8) 50.0(0.0) 50.0(0.0) 58.0(10.3) 60.5(9.0) 74.0(15.6)
CBF 87.2(0.7) 99.4(0.1) 99.5(0.3) 94.7(1.2) 33.2(0.1) 33.2(0.1) 82.0(20.5) 95.7(1.0) 89.0(4.9)
Car 76.7(2.6) 90.5(1.4) 92.5(1.4) 75.8(2.0) 24.0(2.7) 31.7(0.0) 73.0(3.0) 78.2(1.2) 78.3(4.0)
ChlorineConcentration 80.2(1.1) 81.4(0.9) 84.4(1.0) 57.3(1.1) 53.3(0.0) 53.3(0.0) 64.3(3.8) 60.0(0.8) 55.3(0.3)
CinC_ECG_torso 84.0(1.0) 82.4(1.2) 82.6(2.4) 91.1(2.7) 38.1(28.0) 25.0(0.1) 73.6(15.2) 74.5(4.9) 30.0(2.9)
Coffee 99.6(1.1) 100.0(0.0) 100.0(0.0) 97.9(1.8) 51.4(3.5) 53.6(0.0) 98.2(2.5) 99.6(1.1) 97.1(2.8)
Computers 56.3(1.6) 82.2(1.0) 81.5(1.2) 57.4(2.2) 52.2(4.8) 50.0(0.0) 55.9(3.3) 54.8(1.5) 62.9(4.1)
Cricket_X 59.1(1.1) 79.2(0.7) 79.1(0.6) 69.4(1.6) 18.9(23.8) 7.4(0.0) 49.5(5.3) 55.2(2.9) 62.2(2.1)
Cricket_Y 60.0(0.8) 78.7(1.2) 80.3(0.8) 67.5(1.0) 18.4(22.0) 8.5(0.0) 49.7(4.3) 57.0(2.4) 65.6(1.3)
Cricket_Z 61.7(0.8) 81.1(1.0) 81.2(1.4) 69.2(1.0) 18.3(24.4) 6.2(0.0) 49.8(3.6) 48.8(2.8) 62.2(2.3)
DiatomSizeReduction 91.0(1.4) 31.3(3.6) 30.1(0.2) 91.3(1.8) 30.1(0.7) 30.1(0.0) 70.3(28.9) 95.4(0.7) 88.0(6.6)
DistalPhalanxOutlineAgeGroup 65.7(1.1) 71.0(1.3) 71.7(1.3) 73.7(1.6) 46.8(0.0) 44.6(2.3) 74.4(2.2) 75.2(1.4) 71.0(2.1)
DistalPhalanxOutlineCorrect 72.6(1.3) 76.0(1.5) 77.1(1.0) 74.1(1.4) 58.3(0.0) 58.3(0.0) 75.3(1.8) 75.9(2.0) 71.3(1.0)
DistalPhalanxTW 61.7(1.3) 69.0(2.1) 66.5(1.6) 68.8(1.6) 30.2(0.0) 28.3(0.7) 67.7(1.8) 67.3(2.8) 60.9(3.0)
ECG200 91.6(0.7) 88.9(1.0) 87.4(1.9) 92.3(1.1) 64.0(0.0) 64.0(0.0) 83.3(3.9) 81.4(1.3) 84.2(5.1)
ECG5000 92.9(0.1) 94.0(0.1) 93.4(0.2) 94.0(0.2) 61.8(10.9) 58.4(0.0) 93.7(0.6) 92.8(0.2) 91.9(0.2)
ECGFiveDays 97.0(0.5) 98.7(0.3) 97.5(1.9) 98.2(0.7) 49.9(0.3) 49.7(0.0) 76.2(13.4) 88.2(1.8) 69.8(14.1)
Earthquakes 71.7(1.3) 72.7(1.7) 71.2(2.0) 74.8(0.7) 74.8(0.0) 74.8(0.0) 74.9(0.2) 70.0(1.9) 74.8(0.0)
ElectricDevices 59.2(1.1) 70.2(1.2) 72.9(0.9) 67.4(1.1) 33.6(19.8) 24.2(0.0) 64.4(1.2) 68.1(1.0) 60.7(0.7)
FISH 84.8(0.8) 95.8(0.6) 97.9(0.8) 86.6(0.9) 13.4(1.3) 12.6(0.0) 75.8(3.9) 84.9(0.5) 87.5(3.4)
FaceAll 79.3(1.1) 94.5(0.9) 83.9(2.0) 79.3(0.8) 17.0(19.5) 8.0(0.0) 71.7(2.3) 76.8(1.1) 65.7(2.5)
FaceFour 84.0(1.4) 92.8(0.9) 95.5(0.0) 81.5(2.6) 26.8(5.7) 29.5(0.0) 71.2(13.5) 90.6(1.1) 85.5(6.2)
FacesUCR 83.3(0.3) 94.6(0.2) 95.5(0.4) 87.4(0.4) 15.3(2.7) 14.3(0.0) 75.6(5.1) 86.9(0.7) 64.4(2.0)
FordA 73.0(0.4) 90.4(0.2) 92.0(0.4) 92.3(0.3) 51.3(0.0) 51.0(0.8) 79.5(2.6) 88.1(0.7) 52.8(2.1)
FordB 60.3(0.3) 87.8(0.6) 91.3(0.3) 89.0(0.5) 49.8(1.2) 51.2(0.0) 53.3(2.9) 80.6(1.5) 50.3(1.2)
Gun_Point 92.7(1.1) 100.0(0.0) 99.1(0.7) 93.6(3.2) 51.3(3.9) 49.3(0.0) 86.7(9.6) 93.2(1.9) 96.1(2.3)
Ham 69.1(1.4) 71.8(1.4) 75.7(2.7) 72.7(1.2) 50.6(1.4) 51.4(0.0) 73.3(4.2) 71.1(2.0) 72.3(6.3)
HandOutlines 91.8(0.5) 80.6(7.9) 91.1(1.4) 89.9(2.3) 64.1(0.0) 64.1(0.0) 90.9(0.6) 88.8(1.2) 66.0(0.7)
Haptics 43.3(1.4) 48.0(2.4) 51.9(1.2) 42.7(1.6) 20.9(3.5) 20.8(0.0) 40.4(3.3) 36.6(2.4) 40.4(4.5)
Herring 52.8(3.9) 60.8(7.7) 61.9(3.8) 58.6(4.8) 59.4(0.0) 59.4(0.0) 60.0(5.2) 53.9(1.7) 59.1(6.5)
InlineSkate 33.7(1.0) 33.9(0.8) 37.3(0.9) 29.2(0.9) 16.7(1.6) 16.5(1.1) 21.5(2.2) 28.7(1.2) 33.0(6.8)
InsectWingbeatSound 60.7(0.4) 39.3(0.6) 50.7(0.9) 63.3(0.6) 15.8(14.2) 9.1(0.0) 58.3(2.6) 58.3(0.6) 43.7(2.0)
ItalyPowerDemand 95.4(0.2) 96.1(0.3) 96.3(0.4) 96.5(0.5) 50.0(0.2) 49.9(0.0) 95.5(1.9) 95.5(0.4) 88.0(2.2)
LargeKitchenAppliances 47.3(0.6) 90.2(0.4) 90.0(0.5) 61.9(2.6) 41.0(16.5) 33.3(0.0) 43.4(2.8) 66.6(5.0) 77.9(1.8)
Lighting2 67.0(2.1) 73.9(1.4) 77.0(1.7) 69.2(4.6) 55.7(5.2) 54.1(0.0) 63.0(5.9) 63.6(2.5) 70.3(4.1)
Lighting7 63.0(1.7) 82.7(2.3) 84.5(2.0) 62.5(2.3) 31.0(11.3) 26.0(0.0) 53.4(5.9) 65.1(3.3) 66.4(6.6)
MALLAT 91.8(0.6) 96.7(0.9) 97.2(0.3) 87.6(2.0) 13.5(3.7) 12.3(0.1) 90.1(5.7) 92.0(0.7) 59.6(9.8)
Meat 89.7(1.7) 85.3(6.9) 96.8(2.5) 74.2(11.0) 33.3(0.0) 33.3(0.0) 70.5(8.8) 90.2(1.8) 96.8(2.0)
MedicalImages 72.1(0.7) 77.9(0.4) 77.0(0.7) 73.4(1.5) 51.4(0.0) 51.4(0.0) 64.0(1.4) 67.6(1.1) 64.9(2.7)
MiddlePhalanxOutlineAgeGroup 53.1(1.8) 55.3(1.8) 56.9(2.1) 57.9(2.9) 18.8(0.0) 57.1(0.0) 58.5(3.8) 56.6(1.5) 58.1(2.6)
MiddlePhalanxOutlineCorrect 77.0(1.1) 80.1(1.0) 80.9(1.2) 76.1(2.3) 57.0(0.0) 57.0(0.0) 81.1(1.6) 76.6(1.3) 74.4(2.3)
MiddlePhalanxTW 53.4(1.6) 51.2(1.8) 48.4(2.0) 59.2(1.0) 27.3(0.0) 28.6(0.0) 58.1(2.4) 54.9(1.7) 53.9(2.9)
MoteStrain 85.8(0.9) 93.7(0.5) 92.8(0.5) 84.0(1.0) 50.8(4.0) 53.9(0.0) 76.5(14.4) 88.2(0.9) 78.5(4.2)
NonInvasiveFatalECG_Thorax1 91.6(0.4) 95.6(0.3) 94.5(0.3) 91.6(0.4) 16.1(29.3) 2.9(0.0) 90.5(1.2) 86.5(0.5) 49.4(4.2)
NonInvasiveFatalECG_Thorax2 91.7(0.3) 95.3(0.3) 94.6(0.3) 93.2(0.9) 16.0(29.2) 2.9(0.0) 91.5(1.5) 89.8(0.3) 52.5(3.2)
OSULeaf 55.7(1.0) 97.7(0.9) 97.9(0.8) 57.6(2.0) 24.3(12.8) 18.2(0.0) 37.8(4.6) 46.2(2.7) 59.5(5.4)
OliveOil 66.7(3.8) 72.3(16.6) 83.0(8.5) 40.0(0.0) 38.0(4.2) 38.0(4.2) 40.0(0.0) 40.0(0.0) 79.0(6.1)
PhalangesOutlinesCorrect 73.5(2.1) 82.0(0.5) 83.9(1.2) 76.7(1.4) 61.3(0.0) 61.3(0.0) 80.3(1.1) 77.1(4.7) 65.4(0.4)
Phoneme 9.6(0.3) 32.5(0.5) 33.4(0.7) 17.2(0.8) 13.2(4.0) 11.3(0.0) 13.0(1.0) 9.5(0.3) 12.8(1.4)
Plane 97.8(0.5) 100.0(0.0) 100.0(0.0) 97.6(0.8) 13.0(4.5) 13.4(1.4) 96.5(3.2) 96.5(1.4) 100.0(0.0)
ProximalPhalanxOutlineAgeGroup 85.6(0.5) 83.1(1.3) 85.3(0.8) 84.4(1.3) 48.8(0.0) 48.8(0.0) 83.8(0.8) 82.8(1.6) 84.4(0.5)
ProximalPhalanxOutlineCorrect 73.3(1.8) 90.3(0.7) 92.1(0.6) 79.1(1.8) 68.4(0.0) 68.4(0.0) 87.3(1.8) 81.2(2.6) 82.1(0.9)
ProximalPhalanxTW 76.7(0.7) 76.7(0.9) 78.0(1.7) 81.2(1.1) 35.1(0.0) 34.6(1.0) 79.7(1.3) 78.3(1.2) 78.1(0.7)
RefrigerationDevices 37.9(2.1) 50.8(1.0) 52.5(2.5) 48.8(1.9) 33.3(0.0) 33.3(0.0) 36.9(3.8) 43.9(1.0) 50.1(1.5)
ScreenType 40.3(1.0) 62.5(1.6) 62.2(1.4) 38.3(2.2) 34.1(2.4) 33.3(0.0) 42.7(1.8) 38.9(0.9) 43.1(4.7)
ShapeletSim 50.3(3.1) 72.4(5.6) 77.9(15.0) 53.0(4.7) 50.0(0.0) 50.0(0.0) 50.7(4.1) 50.0(1.3) 61.7(10.2)
ShapesAll 77.1(0.5) 89.5(0.4) 92.1(0.4) 75.8(0.9) 13.2(24.3) 1.7(0.0) 61.3(5.3) 61.9(0.9) 62.9(2.6)
SmallKitchenAppliances 37.1(1.9) 78.3(1.3) 78.6(0.8) 59.6(1.8) 36.9(11.3) 33.3(0.0) 48.5(3.6) 61.5(2.7) 65.6(1.9)
SonyAIBORobotSurface 67.2(1.3) 96.0(0.7) 95.8(1.3) 74.3(1.9) 44.3(4.5) 42.9(0.0) 65.3(10.9) 68.7(2.3) 63.8(9.9)
SonyAIBORobotSurfaceII 83.4(0.7) 97.9(0.5) 97.8(0.5) 83.9(1.0) 59.4(7.4) 61.7(0.0) 77.4(6.7) 84.1(1.7) 69.7(4.3)
StarLightCurves 94.9(0.2) 96.1(0.9) 97.2(0.3) 95.7(0.5) 65.4(16.1) 57.7(0.0) 93.9(1.2) 92.6(0.2) 85.0(0.2)
Strawberry 96.1(0.5) 97.2(0.3) 98.1(0.4) 94.6(0.9) 64.3(0.0) 64.3(0.0) 95.6(0.6) 95.9(0.3) 89.5(2.0)
SwedishLeaf 85.1(0.5) 96.9(0.5) 95.6(0.4) 93.0(1.1) 11.8(13.2) 6.5(0.4) 84.6(3.6) 88.4(1.1) 82.5(1.4)
Symbols 83.2(1.0) 95.5(1.0) 90.6(2.3) 82.1(1.9) 22.6(16.9) 17.4(0.0) 75.6(11.5) 81.0(0.7) 75.0(8.8)
ToeSegmentation1 58.3(0.9) 96.1(0.5) 96.3(0.6) 65.9(2.6) 50.5(2.7) 52.6(0.0) 49.0(2.5) 59.5(2.2) 86.5(3.2)
ToeSegmentation2 74.5(1.9) 88.0(3.3) 90.6(1.7) 79.5(2.8) 63.2(30.9) 81.5(0.0) 44.3(15.2) 73.8(2.8) 84.2(4.6)
Trace 80.7(0.7) 100.0(0.0) 100.0(0.0) 96.0(1.8) 35.4(27.7) 24.0(0.0) 86.3(5.4) 95.0(2.5) 95.9(1.9)
TwoLeadECG 76.2(1.3) 100.0(0.0) 100.0(0.0) 86.3(2.6) 50.0(0.0) 50.0(0.0) 76.0(16.8) 87.2(2.1) 85.2(11.5)
Two_Patterns 94.6(0.3) 87.1(0.3) 100.0(0.0) 100.0(0.0) 40.3(31.1) 25.9(0.0) 97.8(0.6) 99.2(0.3) 87.1(1.1)
UWaveGestureLibraryAll 95.5(0.2) 81.7(0.3) 86.0(0.4) 95.4(0.1) 28.9(34.7) 12.8(0.2) 92.9(1.1) 91.8(0.4) 55.6(2.5)
Wine 56.5(7.1) 58.7(8.3) 74.4(8.5) 50.0(0.0) 50.0(0.0) 50.0(0.0) 50.0(0.0) 51.7(5.1) 75.9(9.1)
WordsSynonyms 59.8(0.8) 56.4(1.2) 62.2(1.5) 61.3(0.9) 28.4(13.6) 21.9(0.0) 46.3(6.1) 56.6(0.8) 49.0(3.0)
Worms 45.7(2.4) 76.5(2.2) 79.1(2.5) 57.1(3.7) 42.9(0.0) 42.9(0.0) 42.6(5.5) 38.3(2.5) 46.6(4.5)
WormsTwoClass 60.1(1.5) 72.6(2.7) 74.7(3.3) 63.9(4.4) 57.1(0.0) 55.7(4.5) 57.0(1.9) 53.8(2.6) 57.0(2.3)
synthetic_control 97.6(0.4) 98.5(0.3) 99.8(0.2) 99.6(0.3) 29.8(27.8) 16.7(0.0) 98.3(1.2) 99.0(0.4) 87.4(1.6)
uWaveGestureLibrary_X 76.7(0.3) 75.4(0.4) 78.0(0.4) 78.6(0.4) 18.9(21.3) 12.5(0.4) 71.1(1.5) 71.1(1.1) 60.6(1.5)
uWaveGestureLibrary_Y 69.8(0.2) 63.9(0.6) 67.0(0.7) 69.6(0.6) 23.7(24.0) 12.1(0.0) 63.6(1.2) 62.6(0.7) 52.0(2.1)
uWaveGestureLibrary_Z 69.7(0.2) 72.6(0.5) 75.0(0.4) 71.1(0.5) 18.0(18.4) 12.1(0.0) 65.0(1.8) 64.2(0.9) 56.5(2.0)
wafer 99.6(0.0) 99.7(0.0) 99.9(0.1) 99.6(0.0) 91.3(4.4) 89.2(0.0) 99.2(0.3) 96.1(0.1) 91.4(0.5)
yoga 85.5(0.4) 83.9(0.7) 87.0(0.9) 82.0(0.6) 53.6(0.0) 53.6(0.0) 76.2(3.9) 78.1(0.7) 60.7(1.9)
Average_Rank 4.611765 2.682353 1.994118 3.682353 8.017647 8.417647 5.376471 4.970588 5.247059
Wins 4 18 41 10 0 0 3 4 1

The following table contains the averaged accuracy over 10 runs of each implemented model on the MTS archive, with the standard deviation between parentheses.

Datasets MLP FCN ResNet Encoder MCNN t-LeNet MCDCNN Time-CNN TWIESN
AUSLAN 93.3(0.5) 97.5(0.4) 97.4(0.3) 93.8(0.5) 1.1(0.0) 1.1(0.0) 85.4(2.7) 72.6(3.5) 72.4(1.6)
ArabicDigits 96.9(0.2) 99.4(0.1) 99.6(0.1) 98.1(0.1) 10.0(0.0) 10.0(0.0) 95.9(0.2) 95.8(0.3) 85.3(1.4)
CMUsubject16 60.0(16.9) 100.0(0.0) 99.7(1.1) 98.3(2.4) 53.1(4.4) 51.0(5.3) 51.4(5.0) 97.6(1.7) 89.3(6.8)
CharacterTrajectories 96.9(0.2) 99.0(0.1) 99.0(0.2) 97.1(0.2) 5.4(0.8) 6.7(0.0) 93.8(1.7) 96.0(0.8) 92.0(1.3)
ECG 74.8(16.2) 87.2(1.2) 86.7(1.3) 87.2(0.8) 67.0(0.0) 67.0(0.0) 50.0(17.9) 84.1(1.7) 73.7(2.3)
JapaneseVowels 97.6(0.2) 99.3(0.2) 99.2(0.3) 97.6(0.6) 9.2(2.5) 23.8(0.0) 94.4(1.4) 95.6(1.0) 96.5(0.7)
KickvsPunch 61.0(12.9) 54.0(13.5) 51.0(8.8) 61.0(9.9) 54.0(9.7) 50.0(10.5) 56.0(8.4) 62.0(6.3) 67.0(14.2)
Libras 78.0(1.0) 96.4(0.7) 95.4(1.1) 78.3(0.9) 6.7(0.0) 6.7(0.0) 65.1(3.9) 63.7(3.3) 79.4(1.3)
NetFlow 55.0(26.1) 89.1(0.4) 62.7(23.4) 77.7(0.5) 77.9(0.0) 72.3(17.6) 63.0(18.2) 89.0(0.9) 94.5(0.4)
UWave 90.1(0.3) 93.4(0.3) 92.6(0.4) 90.8(0.4) 12.5(0.0) 12.5(0.0) 84.5(1.6) 85.9(0.7) 75.4(6.3)
Wafer 89.4(0.0) 98.2(0.5) 98.9(0.4) 98.6(0.2) 89.4(0.0) 89.4(0.0) 65.8(38.1) 94.8(2.1) 94.9(0.6)
WalkvsRun 70.0(15.8) 100.0(0.0) 100.0(0.0) 100.0(0.0) 75.0(0.0) 60.0(24.2) 45.0(25.8) 100.0(0.0) 94.4(9.1)
Average_Rank 5.208333 2.000000 2.875000 3.041667 7.583333 8.000000 6.833333 4.625000 4.833333
Wins 0 5 3 0 0 0 0 0 2

These results should give an insight of deep learning for TSC therefore encouraging researchers to consider the DNNs as robust classifiers for time series data.

If you would like to generate the critical difference diagrams using Wilcoxon Signed Rank test with Holm's alpha correction, check out the cd-diagram repository.

Reference

If you re-use this work, please cite:

@article{IsmailFawaz2018deep,
  Title                    = {Deep learning for time series classification: a review},
  Author                   = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
  journal                  = {Data Mining and Knowledge Discovery},
  Year                     = {2019},
  volume                   = {33},
  number                   = {4},
  pages                    = {917--963},
}

Acknowledgement

We would like to thank the providers of the UCR/UEA archive. We would also like to thank NVIDIA Corporation for the Quadro P6000 grant and the Mésocentre of Strasbourg for providing access to the cluster. We would also like to thank François Petitjean and Charlotte Pelletier for the fruitful discussions, their feedback and comments while writing this paper.

Owner
Hassan ISMAIL FAWAZ
Machine Learning Researcher - PhD in Computer Science.
Hassan ISMAIL FAWAZ
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 05, 2023
RoFormer_pytorch

PyTorch RoFormer 原版Tensorflow权重(https://github.com/ZhuiyiTechnology/roformer) chinese_roformer_L-12_H-768_A-12.zip (提取码:xy9x) 已经转化为PyTorch权重 chinese_r

yujun 283 Dec 12, 2022
Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

CSRL Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning Python: 3

4 Apr 14, 2022
PyTorch implementation of Pointnet2/Pointnet++

Pointnet2/Pointnet++ PyTorch Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to iss

Erik Wijmans 1.2k Dec 29, 2022
Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem

Benchmarking nearest neighbors Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem, but so far t

Erik Bernhardsson 3.2k Jan 03, 2023
Hitters Linear Regression - Hitters Linear Regression With Python

Hitters_Linear_Regression Kullanacağımız veri seti Carnegie Mellon Üniversitesi'

AyseBuyukcelik 2 Jan 26, 2022
Analysis of rationale selection in neural rationale models

Neural Rationale Interpretability Analysis We analyze the neural rationale models proposed by Lei et al. (2016) and Bastings et al. (2019), as impleme

Yiming Zheng 3 Aug 31, 2022
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

klein 125 Jan 03, 2023
A treasure chest for visual recognition powered by PaddlePaddle

简体中文 | English PaddleClas 简介 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 近期更新 2021.11.1 发布PP-ShiTu技术报告,新增饮料识别demo 2021.10.23 发

4.6k Dec 31, 2022
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields

MINE: Continuous-Depth MPI with Neural Radiance Fields Project Page | Video PyTorch implementation for our ICCV 2021 paper. MINE: Towards Continuous D

Zijian Feng 325 Dec 29, 2022
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
TCTrack: Temporal Contexts for Aerial Tracking (CVPR2022)

TCTrack: Temporal Contexts for Aerial Tracking (CVPR2022) Ziang Cao and Ziyuan Huang and Liang Pan and Shiwei Zhang and Ziwei Liu and Changhong Fu In

Intelligent Vision for Robotics in Complex Environment 100 Dec 19, 2022
Multilingual Image Captioning

Multilingual Image Captioning Authors: Bhavitvya Malik, Gunjan Chhablani Demo Link: https://huggingface.co/spaces/flax-community/multilingual-image-ca

Gunjan Chhablani 32 Nov 25, 2022
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Jina AI 2 Mar 15, 2022
FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data, a relatively complete set of integrated multi-source data download terminal software fast is developed. The softw

ChangChuntao 23 Dec 31, 2022
MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration

MVP Benchmark: Multi-View Partial Point Clouds for Completion and Registration [NEWS] 2021-07-12 [NEW 🎉 ] The submission on Codalab starts! 2021-07-1

PL 93 Dec 21, 2022
Bianace Prediction Pytorch Model

Bianace Prediction Pytorch Model Main Results ETHUSDT from 2021-01-01 00:00:00 t

RoyYang 4 Jul 20, 2022
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens

MSG-Transformer Official implementation of the paper MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens, by Jiemin

Hust Visual Learning Team 68 Nov 16, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022