Pytorch0.4.1 codes for InsightFace

Last update: Jan 1, 2023

Related tags

Deep Learning InsightFace_Pytorch

Overview

InsightFace_Pytorch

Pytorch0.4.1 codes for InsightFace

1. Intro

This repo is a reimplementation of Arcface(paper), or Insightface(github)
For models, including the pytorch implementation of the backbone modules of Arcface and MobileFacenet
Codes for transform MXNET data records in Insightface(github) to Image Datafolders are provided
Pretrained models are posted, include the MobileFacenet and IR-SE50 in the original paper

2. Pretrained Models & Performance

IR-SE50 @ BaiduNetdisk, IR-SE50 @ Onedrive

LFW(%)	CFP-FF(%)	CFP-FP(%)	AgeDB-30(%)	calfw(%)	cplfw(%)	vgg2_fp(%)
0.9952	0.9962	0.9504	0.9622	0.9557	0.9107	0.9386

Mobilefacenet @ BaiduNetDisk, Mobilefacenet @ OneDrive

LFW(%)	CFP-FF(%)	CFP-FP(%)	AgeDB-30(%)	calfw(%)	cplfw(%)	vgg2_fp(%)
0.9918	0.9891	0.8986	0.9347	0.9402	0.866	0.9100

3. How to use

clone

git clone https://github.com/TropComplique/mtcnn-pytorch.git

3.1 Data Preparation

3.1.1 Prepare Facebank (For testing over camera or video)

Provide the face images your want to detect in the data/face_bank folder, and guarantee it have a structure like following:

data/facebank/
        ---> id1/
            ---> id1_1.jpg
        ---> id2/
            ---> id2_1.jpg
        ---> id3/
            ---> id3_1.jpg
           ---> id3_2.jpg

3.1.2 download the pretrained model to work_space/model

If more than 1 image appears in one folder, an average embedding will be calculated

3.2.3 Prepare Dataset ( For training)

download the refined dataset: (emore recommended)

emore dataset @ BaiduDrive, emore dataset @ Dropbox
More Dataset please refer to the original post

Note: If you use the refined MS1M dataset and the cropped VGG2 dataset, please cite the original papers.

after unzip the files to 'data' path, run :
```
python prepare_data.py
```
after the execution, you should find following structure:

faces_emore/
            ---> agedb_30
            ---> calfw
            ---> cfp_ff
            --->  cfp_fp
            ---> cfp_fp
            ---> cplfw
            --->imgs
            ---> lfw
            ---> vgg2_fp

3.2 detect over camera:

1. download the desired weights to model folder:
IR-SE50 @ BaiduNetdisk
IR-SE50 @ Onedrive
Mobilefacenet @ BaiduNetDisk
Mobilefacenet @ OneDrive
2 to take a picture, run
```
python take_pic.py -n name
```
press q to take a picture, it will only capture 1 highest possibility face if more than 1 person appear in the camera
3 or you can put any preexisting photo into the facebank directory, the file structure is as following:

- facebank/
         name1/
             photo1.jpg
             photo2.jpg
             ...
         name2/
             photo1.jpg
             photo2.jpg
             ...
         .....
    if more than 1 image appears in the directory, average embedding will be calculated

4 to start
```
python face_verify.py 
```

3.3 detect over video:

```
python infer_on_video.py -f [video file name] -s [save file name]
```

the video file should be inside the data/face_bank folder

Video Detection Demo @Youtube

3.4 Training:

```
python train.py -b [batch_size] -lr [learning rate] -e [epochs]

# python train.py -net mobilefacenet -b 200 -w 4
```

4. References

This repo is mainly inspired by deepinsight/insightface and InsightFace_TF

PS

PRs are welcome, in case that I don't have the resource to train some large models like the 100 and 151 layers model
Email : [email protected]

Comments

training paras for mobilefacenet

Hi @TreB1eN ,

I found this line in the config.py file: conf.milestones = [3,4,5] # mobildefacenet but the milestones is not used during training, i guess that means learning rate is not decay. Learner.py +line 225: seems like self.schedule_lr() should be called during training according to the paper for mobilefacenet.

BTW, would you please share the training paras for mobilefacenet to reproduce your acc? batch size,init learning rate.

Thanks for your help!

opened by cvtower 24
Maybe a bug

Hi @TreB1eN , after reading the code, I found the follow codes may be a bug of your Arcloss. cond_mask = cond_v > 0 keep_val = (cos_theta - self.mm) # when theta not in [0,pi], use cosface instead cos_theta_m[cond_mask] = keep_val[cond_mask]
I think it should be : cond_mask = cond_v <= 0 . In fact, you implement a special Am-softmax with margin = mm, instead of ArcLoss.

opened by JingyuanHu 14
I met some environment problem when I used conda install --yes --file requirements.txt
It's python3.6 on ubuntu16

Solving environment: failed with current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: failed

PackagesNotFoundError: The following packages are not available from current channels:

mxnet==1.2.1.post1

opencv_python==3.4.0.12

easydict==1.7

scikit_learn==0.19.2

mxnet_cu90==1.2.1

torch==0.4.0

Current channels:

https://mirrors.ustc.edu.cn/anaconda/pkgs/main/linux-64

https://mirrors.ustc.edu.cn/anaconda/pkgs/main/noarch

https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/linux-64

https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/noarch

https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/linux-64

https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/noarch

https://repo.anaconda.com/pkgs/main/linux-64

https://repo.anaconda.com/pkgs/main/noarch

https://repo.anaconda.com/pkgs/r/linux-64

https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're looking for, navigate to

https://anaconda.org

and use the search bar at the top of the page.

and it is the same problem on windows. could you help me?
opened by liguoyu666 10
Some errors about image color

Hi, Thanks for your great contribute on this open source work! But I have a few questions! Firstly, I downloaded the faces_webface_112x112.zip from InsightFace Dataset zoo. Then I unzipped the data and followed your instruction to generate training imgs but I got such images! It looks like an issue about RGB channel and BGR channel. So i wonder whether its color is correct. Thank you!

opened by lvvvmd 8
MTCNN problem

MTCNN code changes in this repo compared to original TropComplique repo have reduced accuracy. I have checked that by replacing detector.py, first_stage.py and get_nets.py, I can improve accuracy significantly. Applies to mtcnn.py too.

Any idea what could be causing this drop in accuracy?

opened by kulkarniniraj 7
I get the acc 56% by 5 epoch which make me saaaaaaaaaaad

with epoch = 4 , batch_size = 256 , number workers = 3 , others parameter is the same as author. and with 4 GPUs training ms1m in my server . finally , get the accuracy rate = 56% , it's a very sad result. I don't know where I got wrong.
I don't believe the cause is the such short epoch ~~~

[email protected]

opened by ruiming46zrm 7

Fine-tuning pretrained model

Thanks @TreB1eN for the great work! I was trying to fine-tune on a small dataset by the pretraind model IR-SE50. In this process, I do not train the embedding model, but add the nn.Linear layer to do softmax classification.

There are the related code. The input of nn.Linear is embeddings before normed.

class Arcface(Module):
    # just fc, use ArcFace embedding model
    def __init__(self, embedding_size=512, classnum=28, pretrained=None):
        super(Arcface, self).__init__()
        self.backbone = Backbone(50, 0.6, 'ir_se')
        self.fc = Linear(embedding_size, classnum)
        if pretrained:
            self.backbone.load_state_dict(torch.load(pretrained))

    def forward(self, input):
        embeddings, linear = self.backbone(input)
        logits = self.fc(linear)
        return embeddings, logits

The forward part of Backbone is also changed as follows:

class Backbone(Module):
    def __init__(self, num_layers, drop_ratio, mode='ir'):
        super(Backbone, self).__init__()
        assert num_layers in [
            50, 100, 152], 'num_layers should be 50,100, or 152'
        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
        blocks = get_blocks(num_layers)
        if mode == 'ir':
            unit_module = bottleneck_IR
        elif mode == 'ir_se':
            unit_module = bottleneck_IR_SE
        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
                                      BatchNorm2d(64),
                                      PReLU(64))
        self.output_layer = Sequential(BatchNorm2d(512),
                                       Dropout(drop_ratio),
                                       Flatten(),
                                       Linear(512 * 7 * 7, 512),
                                       BatchNorm1d(512))
        modules = []
        for block in blocks:
            for bottleneck in block:
                modules.append(
                    unit_module(bottleneck.in_channel,
                                bottleneck.depth,
                                bottleneck.stride))
        self.body = Sequential(*modules)

    def forward(self, x):
        x = self.input_layer(x)
        x = self.body(x)
        x = self.output_layer(x)
        return l2_norm(x), x

However, in the training process, the loss is downing very slow. The lr is 1e-3, and the optimizer is Adam.

Epoch 1/20
----------
The train loss is 3.353309392929077, The accuracy is 0.0133928582072258

Epoch 2/20
----------
The train loss is 3.2458348274230957, The accuracy is 0.2031250149011612

Epoch 3/20
----------
The train loss is 3.152858257293701, The accuracy is 0.3125

Epoch 4/20
----------
The train loss is 3.0687711238861084, The accuracy is 0.3504464328289032

Epoch 5/20
----------
The train loss is 2.9923739433288574, The accuracy is 0.3638392984867096

Could you give me some advice, or point out where my mistakes are?

opened by yuhaoooo 6

No such file or directory: 'work_space/save/model_final.pth'

Hi there,

When I issue "python face_verify.py" there will be the following error message:

$ python face_verify.py
mtcnn loaded
{'batch_size': 100, 'emore_folder': PosixPath('data/faces_emore'), 'test_transform': Compose(
    ToTensor()
    Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
), 'embedding_size': 512, 'face_limit': 10, 'data_mode': 'emore', 'ms1m_folder': PosixPath('data/faces_ms1m_112x112'), 'work_path': PosixPath('work_space'), 'log_path': PosixPath('work_space/log'), 'net_depth': 50, 'facebank_path': PosixPath('data/facebank'), 'net_mode': 'ir_se', 'drop_ratio': 0.6, 'vgg_folder': PosixPath('data/faces_vgg_112x112'), 'device': device(type='cuda', index=0), 'save_path': PosixPath('work_space/save'), 'min_face_size': 30, 'threshold': 1.5, 'model_path': PosixPath('work_space/models'), 'use_mobilfacenet': False, 'input_size': [112, 112], 'data_path': PosixPath('data')}
ir_se_50 model generated
Traceback (most recent call last):
  File "face_verify.py", line 31, in <module>
    learner.load_state(conf, 'final.pth', True, True)
  File "/home/robert/workingbox/InsightFace_Pytorch/Learner.py", line 82, in load_state
    self.model.load_state_dict(torch.load(save_path/'model_{}'.format(fixed_str)))
  File "/home/robert/inf1/lib/python3.5/site-packages/torch/serialization.py", line 356, in load
    f = open(str(f), 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'work_space/save/model_final.pth'

My system info as follows:

OS: Ubuntu 16.04.5 LTS x86-64
CUDA: release 9.0, V9.0.176
Python: 3.5.2
torch: 0.4.1
torchvision: 0.2.1
mxnet: 1.3.1
mxnet_cu90: 1.3.1
tensorboardX: 1.4
opencv-python: 3.4.4.19

How can I generate or get the file "model_final.pth" ?

Thanks

opened by while-true-work 5

GPU memory required to train r100

Thank you for your great work! However, I met a problem about limitation of GPU memory. Can you give me some suggestion about the minimum GPU memory required to train r100 ArcFace by your code?

opened by JingyuanHu 5
data form for evaluating

how change" agedb_30, agedb_30_issame = get_val_pair(data_path, 'agedb_30')" for my own dataset (img fold and labels). len(agedb_30) = 12000, but len(agedb_30_issame)=6000, and sum(agedb_30_issame ) =3000, and is random. can you give some interpreation? thx.

opened by foocker 4
Environment setting problem(POSIXPATH error)

I've clone this code and run pip3 -r requirement.txt

But I met some problem due to invalid arguments

ex:
os.path.join didn't support POSIXPATH

my environment is ubuntu 16.0.4 python 3.5.1

opened by ming1144 4
Bump pillow from 5.2.0 to 9.3.0
Bumps pillow from 5.2.0 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
How to train next

model: mobileface device: NVIDIA 2080 * 1 dataSet: glint360K Based on the pre training model, continue training for about 30 days loss is reduced only in a few iterations after it is reduced, and then it is almost the same

opened by momohuangsha 1
Bump numpy from 1.14.5 to 1.22.0
Bumps numpy from 1.14.5 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Details of alignment by MTCNN

I am working with CFP-FP dataset. When I use data that is provided by Insight-face I get above the reported accuracy. However, When I align CFP myself with mtcnn, the performance drops. Could you please provide information about the preprocessing?

opened by msedebrahimi 0

Owner

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Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

127 Jan 4, 2023

Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Zero-shot-Fact-Verification-by-Claim-Generation This repository contains code and models for the paper: Zero-shot Fact Verification by Claim Generatio

47 Jan 1, 2023

The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

26 Dec 26, 2022

Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 4, 2022

The codes and models in 'Gaze Estimation using Transformer'.

GazeTR We provide the code of GazeTR-Hybrid in "Gaze Estimation using Transformer". We recommend you to use data processing codes provided in GazeHub.

65 Dec 27, 2022

codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification

DLCF-DCA codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted t

15 Aug 30, 2022

The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

869 Jan 7, 2023

The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for

124 Dec 27, 2022

This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations at CVPR'21. According to some product reasons, we are not planning to release the training/testing codes and models. However, we will release the dataset and the scripts to prepare the dataset.

TransFill-Reference-Inpainting This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transf

80 Dec 8, 2022

Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021

Towards Diverse Paragraph Captioning for Untrimmed Videos This repository contains PyTorch implementation of our paper Towards Diverse Paragraph Capti

61 Oct 11, 2022

Pytorch0.4.1 codes for InsightFace

Related tags

Overview

InsightFace_Pytorch

1. Intro

2. Pretrained Models & Performance

3. How to use

3.1 Data Preparation

3.1.1 Prepare Facebank (For testing over camera or video)

3.1.2 download the pretrained model to work_space/model

3.2.3 Prepare Dataset ( For training)

3.2 detect over camera:

3.3 detect over video:

3.4 Training:

4. References

PS

Comments

9.3.0

Changes

9.3.0 (2022-10-29)

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Owner

Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"

This is my codes that can visualize the psnr image in testing videos.

codes for Image Inpainting with External-internal Learning and Monochromic Bottleneck

Source codes for "Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs"

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

Python codes for Lite Audio-Visual Speech Enhancement.

Codes for our IJCAI21 paper: Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Official codes for the paper "Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech"

Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

The codes and models in 'Gaze Estimation using Transformer'.

codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification

The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio