EfficientFace

Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI'21

Requirements

Python >= 3.6
PyTorch >= 1.2
torchvision >= 0.4.0

Training

Step 1: download basic emotions dataset of RAF-DB, and make sure it has the structure like the following:

./RAF-DB/
         train/
               0/
                 train_09748.jpg
                 ...
                 train_12271.jpg
               1/
               ...
               6/
         test/
              0/
              ...
              6/

[Note] 0: Neutral; 1: Happiness; 2: Sadness; 3: Surprise; 4: Fear; 5: Disgust; 6: Anger

Step 2: download pre-trained model from Google Drive, and put it into ./checkpoint.
Step 3: change the --data in run.sh to your path
Step 4: run sh run.sh

Pre-trained Models

Sept. 16, 2021 Update
We provide the pre-trained ResNet-18 and ResNet-50 on MS-Celeb-1M (classes number is 12666) for your research.
The Google Driver for ResNet-18 model. The Google Driver for ResNet-50 model.
The pre-trained ResNet-50 model can be also used for LDG.
Nov. 6, 2021 Update
The fine-tuned LDG models on CAER-S, AffectNet-7, and AffectNet-8 can be downloaded here, here, and here, respectively.
Nov. 12, 2021 Update
The trained EfficientFace model on RAF-DB, CAER-S, AffectNet-7, and AffectNet-8 can be downloaded here, here, here, and here, respectively. As demonstrated in the paper, the testing accuracy is 88.36%, 85.87%, 63.70%, and 59.89%, respectively.

Citation

@inproceedings{zhao2021robust,
  title={Robust Lightweight Facial Expression Recognition Network with Label Distribution Training},
  author={Zhao, Zengqun and Liu, Qingshan and Zhou, Feng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={4},
  pages={3510--3519},
  year={2021}
}

Note

The samples' number of the CAER-S dataset employed in our work should be: all (69,982 samples), training set (48,995 samples), and test set (20,987 samples). We apologize for the typos in our paper.

great work, when i used the pretrianed weights to show the perfromance , this happend when i made a new depite val script to see the result, as is shown in the code ,the val process used only model_cla to get the result ,when i loaded the Pretrained_EfficientFace.tar in model_cla ,an error occured as below: do you hava any idea?

model_cla.load_state_dict(pre_trained_dict) File "E:\anaconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 1224, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for EfficientFace: Missing key(s) in state_dict: "conv1.0.weight", "conv1.1.weight", "conv1.1.bias", "conv1.1.running_mean", "conv1.1.running_var", "stage2.0.branch1.0.weight", "stage2.0.branch1.1.weight", "stage2.0.branch1.1.bias", "stage2.0.branch1.1.running_mean", "stage2.0.branch1.1.running_var", "stage2.0.branch1.2.weight", "stage2.0.branch1.3.weight", "stage2.0.branch1.3.bias", "stage2.0.branch1.3.running_mean", "stage2.0.branch1.3.running_var", "stage2.0.branch2.0.weight", "stage2.0.branch2.1.weight", "stage2.0.branch2.1.bias", "stage2.0.branch2.1.running_mean", "stage2.0.branch2.1.running_var", "stage2.0.branch2.3.weight", "stage2.0.branch2.4.weight", "stage2.0.branch2.4.bias", "stage2.0.branch2.4.running_mean", 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"modulator.channel_att.gate_c.gate_c_bn_1.bias", "modulator.channel_att.gate_c.gate_c_bn_1.running_mean", "modulator.channel_att.gate_c.gate_c_bn_1.running_var", "modulator.channel_att.gate_c.gate_c_fc_final.weight", "modulator.channel_att.gate_c.gate_c_fc_final.bias", "modulator.spatial_att.gate_s.gate_s_conv_reduce0.weight", "modulator.spatial_att.gate_s.gate_s_conv_reduce0.bias", "modulator.spatial_att.gate_s.gate_s_bn_reduce0.weight", "modulator.spatial_att.gate_s.gate_s_bn_reduce0.bias", "modulator.spatial_att.gate_s.gate_s_bn_reduce0.running_mean", "modulator.spatial_att.gate_s.gate_s_bn_reduce0.running_var", "modulator.spatial_att.gate_s.gate_s_conv_di_0.weight", "modulator.spatial_att.gate_s.gate_s_conv_di_0.bias", "modulator.spatial_att.gate_s.gate_s_bn_di_0.weight", "modulator.spatial_att.gate_s.gate_s_bn_di_0.bias", "modulator.spatial_att.gate_s.gate_s_bn_di_0.running_mean", "modulator.spatial_att.gate_s.gate_s_bn_di_0.running_var", 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Unexpected key(s) in state_dict: "module.conv1.0.weight", "module.conv1.1.weight", "module.conv1.1.bias", "module.conv1.1.running_mean", "module.conv1.1.running_var", "module.conv1.1.num_batches_tracked", "module.stage2.0.branch1.0.weight", "module.stage2.0.branch1.1.weight", "module.stage2.0.branch1.1.bias", "module.stage2.0.branch1.1.running_mean", "module.stage2.0.branch1.1.running_var", "module.stage2.0.branch1.1.num_batches_tracked", "module.stage2.0.branch1.2.weight", "module.stage2.0.branch1.3.weight", "module.stage2.0.branch1.3.bias", "module.stage2.0.branch1.3.running_mean", "module.stage2.0.branch1.3.running_var", "module.stage2.0.branch1.3.num_batches_tracked", "module.stage2.0.branch2.0.weight", "module.stage2.0.branch2.1.weight", "module.stage2.0.branch2.1.bias", "module.stage2.0.branch2.1.running_mean", "module.stage2.0.branch2.1.running_var", "module.stage2.0.branch2.1.num_batches_tracked", "module.stage2.0.branch2.3.weight", "module.stage2.0.branch2.4.weight", "module.stage2.0.branch2.4.bias", "module.stage2.0.branch2.4.running_mean", "module.stage2.0.branch2.4.running_var", "module.stage2.0.branch2.4.num_batches_tracked", "module.stage2.0.branch2.5.weight", "module.stage2.0.branch2.6.weight", "module.stage2.0.branch2.6.bias", "module.stage2.0.branch2.6.running_mean", "module.stage2.0.branch2.6.running_var", "module.stage2.0.branch2.6.num_batches_tracked", "module.stage2.1.branch2.0.weight", "module.stage2.1.branch2.1.weight", "module.stage2.1.branch2.1.bias", "module.stage2.1.branch2.1.running_mean", "module.stage2.1.branch2.1.running_var", "module.stage2.1.branch2.1.num_batches_tracked", "module.stage2.1.branch2.3.weight", "module.stage2.1.branch2.4.weight", "module.stage2.1.branch2.4.bias", "module.stage2.1.branch2.4.running_mean", "module.stage2.1.branch2.4.running_var", "module.stage2.1.branch2.4.num_batches_tracked", "module.stage2.1.branch2.5.weight", "module.stage2.1.branch2.6.weight", "module.stage2.1.branch2.6.bias", "module.stage2.1.branch2.6.running_mean", "module.stage2.1.branch2.6.running_var", "module.stage2.1.branch2.6.num_batches_tracked", "module.stage2.2.branch2.0.weight", "module.stage2.2.branch2.1.weight", "module.stage2.2.branch2.1.bias", "module.stage2.2.branch2.1.running_mean", "module.stage2.2.branch2.1.running_var", "module.stage2.2.branch2.1.num_batches_tracked", "module.stage2.2.branch2.3.weight", "module.stage2.2.branch2.4.weight", "module.stage2.2.branch2.4.bias", "module.stage2.2.branch2.4.running_mean", "module.stage2.2.branch2.4.running_var", "module.stage2.2.branch2.4.num_batches_tracked", "module.stage2.2.branch2.5.weight", "module.stage2.2.branch2.6.weight", "module.stage2.2.branch2.6.bias", "module.stage2.2.branch2.6.running_mean", "module.stage2.2.branch2.6.running_var", "module.stage2.2.branch2.6.num_batches_tracked", "module.stage2.3.branch2.0.weight", "module.stage2.3.branch2.1.weight", "module.stage2.3.branch2.1.bias", "module.stage2.3.branch2.1.running_mean", 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"module.conv5.1.num_batches_tracked", "module.fc.weight", "module.fc.bias".

missing the last fc layer in the pre-trained model

Hi, thanks for the nice and clean repo. I am trying to use your code on some in the wild images. I realize the provided pre-trained model_cla doesn't have the weight of the final FC layer (1024 x 7). Could you help me with that? Thanks again

opened by sukun1045 19

cannot reproduce the result with CARE-S

Thanks for sharing the models.

I try to reproduce the results in the paper with CARE-S dataset. At first, view(-1) does not work in the accuracy func. (325 lines in main.py), so I use reshape(-1) instaed of view(-1). I might think it does not affect the results.

Nevertheless, I can`t get the expected result. The accuracy is quite low even though I use the pre-trained model uploaded in this repo. The accuracy of test set is not bad at first time, but it becomes worse with epochs. The progress is shown below.

Training time: 05-31 16:52
Current learning rate:  0.1
Epoch: [0][  0/157]     Loss 0.2979 (0.2979)    Accuracy 12.500 (12.500)
Epoch: [0][ 10/157]     Loss 0.2384 (0.2485)    Accuracy 19.531 (13.707)
Epoch: [0][ 20/157]     Loss 0.2250 (0.2420)    Accuracy 10.156 (14.174)
Epoch: [0][ 30/157]     Loss 0.2187 (0.2359)    Accuracy 17.188 (14.793)
Epoch: [0][ 40/157]     Loss 0.2107 (0.2317)    Accuracy 17.969 (15.377)
Epoch: [0][ 50/157]     Loss 0.2052 (0.2272)    Accuracy 23.438 (15.395)
Epoch: [0][ 60/157]     Loss 0.2063 (0.2236)    Accuracy 16.406 (15.151)
Epoch: [0][ 70/157]     Loss 0.2074 (0.2214)    Accuracy 16.406 (15.119)
Epoch: [0][ 80/157]     Loss 0.2119 (0.2199)    Accuracy 11.719 (15.239)
Epoch: [0][ 90/157]     Loss 0.2050 (0.2181)    Accuracy 15.625 (15.393)
Epoch: [0][100/157]     Loss 0.2081 (0.2168)    Accuracy 14.844 (15.316)
Epoch: [0][110/157]     Loss 0.2133 (0.2158)    Accuracy 15.625 (15.280)
Epoch: [0][120/157]     Loss 0.2068 (0.2151)    Accuracy 13.281 (15.283)
Epoch: [0][130/157]     Loss 0.2117 (0.2149)    Accuracy 15.625 (15.357)
Epoch: [0][140/157]     Loss 0.2069 (0.2142)    Accuracy 17.188 (15.376)
Epoch: [0][150/157]     Loss 0.2092 (0.2137)    Accuracy 12.500 (15.444)
Test: [0/7]     Loss 1.0344 (1.0344)    Accuracy 57.031 (57.031)
 *** Accuracy 15.067  ***

opened by kiyoungkim1 6

pretrain loaded error

great work, when i used the pretrianed weights to show the perfromance , this happend when i made a new depite val script to see the result, as is shown in the code ,the val process used only model_cla to get the result ,when i loaded the Pretrained_EfficientFace.tar in model_cla ,an error occured as below: do you hava any idea?

model_cla.load_state_dict(pre_trained_dict) File "E:\anaconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 1224, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for EfficientFace: Missing key(s) in state_dict: "conv1.0.weight", "conv1.1.weight", "conv1.1.bias", "conv1.1.running_mean", "conv1.1.running_var", "stage2.0.branch1.0.weight", "stage2.0.branch1.1.weight", "stage2.0.branch1.1.bias", "stage2.0.branch1.1.running_mean", "stage2.0.branch1.1.running_var", "stage2.0.branch1.2.weight", "stage2.0.branch1.3.weight", "stage2.0.branch1.3.bias", "stage2.0.branch1.3.running_mean", "stage2.0.branch1.3.running_var", "stage2.0.branch2.0.weight", "stage2.0.branch2.1.weight", "stage2.0.branch2.1.bias", "stage2.0.branch2.1.running_mean", "stage2.0.branch2.1.running_var", "stage2.0.branch2.3.weight", "stage2.0.branch2.4.weight", "stage2.0.branch2.4.bias", "stage2.0.branch2.4.running_mean", 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opened by yearlz 4

Problem setting up the environment

I'm trying to run the project on my pc, but i keep running into the following error:

Traceback (most recent call last):
  File "D:\LEO\2-Estudos\sem8\tcc2\git reconhecimento de expressoes\EfficientFace\main.py", line 383, in <module>
    main()
  File "D:\LEO\2-Estudos\sem8\tcc2\git reconhecimento de expressoes\EfficientFace\main.py", line 99, in main
    train_dataset = datasets.ImageFolder(traindir,
  File "C:\Users\leodo\AppData\Local\Programs\Python\Python39\lib\site-packages\torchvision\datasets\folder.py", line 310, in __init__
    super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,
  File "C:\Users\leodo\AppData\Local\Programs\Python\Python39\lib\site-packages\torchvision\datasets\folder.py", line 145, in __init__
    classes, class_to_idx = self.find_classes(self.root)
  File "C:\Users\leodo\AppData\Local\Programs\Python\Python39\lib\site-packages\torchvision\datasets\folder.py", line 221, in find_classes
    return find_classes(directory)
  File "C:\Users\leodo\AppData\Local\Programs\Python\Python39\lib\site-packages\torchvision\datasets\folder.py", line 42, in find_classes
    raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
FileNotFoundError: Couldn't find any class folder in D:\LEO\2-Estudos\sem8\tcc2\git reconhecimento de expressoes\EfficientFace\train.

Can you help me?

opened by leodollinger 3

Question about combining channel and spatial heatmaps

Hi, Zeng Qun,

Thank you for sharing this great work. I noticed that in your paper and also in the BAM, the channel heatmaps M_c(F_stage2) and the spatial heatmaps M_s(F_stage2) were "added" before being passed through sigmoid. But in your code, they were "multiplied" before being passed through sigmoid. https://github.com/zengqunzhao/EfficientFace/blob/328cb992915ed8c2dcbae351a8d9da718c117d8f/models/modulator.py#L58 I wonder if there is any interesting reason for you to do it differently ? Thanks.

opened by alvin870203 2
Pretrained LDG got extreme low accuracy on RAF-DB
Hi, zengqun

Thanks for your nice work. I ran the code with the provided models on RAF-DB (I did not modify anything except the data path), however, I got every low accuracy as below:

Current learning rate: 0.00010000000000000003 Epoch: [99][ 0/96] Loss 0.0743 (0.0743) Accuracy 3.906 ( 3.906) Epoch: [99][10/96] Loss 0.0642 (0.0640) Accuracy 0.000 ( 1.420) Epoch: [99][20/96] Loss 0.0661 (0.0641) Accuracy 0.781 ( 1.749) Epoch: [99][30/96] Loss 0.0552 (0.0627) Accuracy 0.781 ( 1.815) Epoch: [99][40/96] Loss 0.0636 (0.0624) Accuracy 3.906 ( 1.925) Epoch: [99][50/96] Loss 0.0739 (0.0627) Accuracy 1.562 ( 1.915) Epoch: [99][60/96] Loss 0.0604 (0.0622) Accuracy 0.781 ( 1.857) Epoch: [99][70/96] Loss 0.0473 (0.0615) Accuracy 1.562 ( 1.816) Epoch: [99][80/96] Loss 0.0638 (0.0612) Accuracy 3.125 ( 1.823) Epoch: [99][90/96] Loss 0.0632 (0.0610) Accuracy 2.344 ( 1.829) Test: [ 0/24] Loss 7.5441 (7.5441) Accuracy 6.250 ( 6.250) Test: [10/24] Loss 16.6608 (14.6800) Accuracy 0.781 ( 2.841) Test: [20/24] Loss 8.7611 (12.6607) Accuracy 0.000 ( 2.009) * Accuracy 1.825 Current best accuracy: 4.269882678985596 27.060322523117065

I believe there might have bugs for the pretrained LDG. Thus, I directly validate its performance on RAF-DB, I found that pretrained LDG got extreme low accuracy. The modified validation code and the experiment log are as below:

if args.evaluate: # validate(val_loader, model_cla, criterion_val, args) validate(val_loader, model_dis, criterion_val, args) return

Training time: 06-21 15:24 Test: [ 0/24] Loss 9.1622 (9.1622) Accuracy 5.469 ( 5.469) Test: [10/24] Loss 20.1950 (17.4116) Accuracy 2.344 ( 3.409) Test: [20/24] Loss 9.6880 (14.8375) Accuracy 0.781 ( 2.604) *** Accuracy 2.347 ***

Could you give me help? Thanks very much!
opened by youcaiSUN 2
Change the order of expressions

If I want to change the order of the categories, for example, 0 is no longer the neutral, but the expression representing sadness, what should I change in the code

opened by DJdjUSER 1
Smaller model size

Hi,

I'm looking into a real-time application where speed is more important than accuracy. So I am interested in your opinion. If you had to decrease the size of the model by 10x, 5x and 2x, where would you truncate the model.

E.g. by decreasing the stages and out channels, but by how much? [1]

[1] model = EfficientFace([4, 8, 4], [29, 116, 232, 464, 1024]) https://github.com/zengqunzhao/EfficientFace/blob/350845c9fb0aae1cf14e728591ebb70b20ae7a22/models/EfficientFace.py#L198

Thanks, Rasmus

opened by Rassibassi 1
Face extraction and alignment of RAF dataset

Hi,

great work, and thanks for sharing. I'm trying to train my own version of the model and I have a quick question.

Did you use the aligned versions from the RAF-DB dataset or did you use a custom face detection and alignment method.

The RAF-DB has two folders original and aligned. From your README it seems that you used the files from the original folder, e.g. test_0001.jpg. In the aligned folder the naming is as follows test_0001_aligned.jpg.

Thanks, Rasmus

opened by Rassibassi 1
EfficientFace trained FER models?

Hi! I see that you recently shared several LDG models trained for FER. Do you have a plan to also share the trained EfficientFace models trained on any FER datasets as per paper?

Thanks!

opened by katerynaCh 1
Ask about Retinaface in image preprocessing

Hi Zengqun Thanks for you share this nice work! I want to know which part of the code reflects that you said in your paper that '' the face region is detected and aligned using Retinaface". I carefully looked at the preprocessing part of the code and it seemed that I could not find it. Sure, I'm just beginning to study facial expression recognition so please forgive me if the problem is too simple. I am very grateful for any replies from you

opened by Dearbreeze 1

“Robust Lightweight Facial Expression Recognition Network with Label Distribution Training”, AAAI 2021.

Related tags

Overview

EfficientFace

Requirements

Training

Pre-trained Models

Citation

Note

Comments

Owner

Zengqun Zhao

Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI

Web service for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation based on OpenFace 2.0

OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

Py-FEAT: Python Facial Expression Analysis Toolbox

Facial detection, landmark tracking and expression transfer library for Windows, Linux and Mac

Facial Expression Detection In The Realtime

Facial expression detector

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python

Automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure

《LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classiﬁcation》(AAAI 2021) GitHub:

Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

[AAAI 2021] MVFNet: Multi-View Fusion Network for Efficient Video Recognition

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021)

Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

Label Mask for Multi-label Classification

A PyTorch implementation of ICLR 2022 Oral paper PiCO: Contrastive Label Disambiguation for Partial Label Learning

Face Detection & Age Gender & Expression & Recognition