Global-Local Attention for Emotion Recognition

Related tags

Deep Learning glamor-net
Overview

Global-Local Attention for Emotion Recognition

Requirements

  • Python 3
  • Install tensorflow (or tensorflow-gpu) >= 2.0.0
  • Install some other packages
pip install cython
pip install opencv-python==4.3.0.36 matplotlib numpy==1.18.5 dlib

Dataset

We provide the NCAER-S dataset with original images and extracted faces (a .txt file with 4 bounding box coordinate) in the NCAERS dataset.

The dataset can be downloaded at Google Drive

Note that the dataset and label should have structure like the followings:

NCAER-S 
│
└───images
│   │
│   └───class_1
│   │   │   img1.jpg
│   │   │   img2.jpg
│   │   │   ...
│   └───class_2
│       │   img1.jpg
│       │   img2.jpg
│       │   ...
│   
└───crop
│   │
│   └───class_1
│   │   │   img1.txt
│   │   │   img2.txt
│   │   │   ...
│   └───class_2
│       │   img1.txt
│       │   img2.txt
│       │   ...

Running

Our code supports these types of execution with argument -m or --mode:

#extract faces from <train, val or test> dataset (specified in config.py)
python run.py -m extract dataset_type=train

#train the model with config specified in the config.py
python run.py -m train 

#evaluate the trained model on the dataset <dataset_type>
python run.py -m eval --dataset_type=test --trained_weights=path/to/weights

Evaluation

Our trained model is available at weights/glamor-net/Model.

  • Firstly, please download the dataset and extract it into "data/" directory.
  • Then specified the path to the test data (images and crop):
config = config.copy({
    'test_images': 'path_to_test_images',
    'test_crop':   'path_to_test_cropped_faces' #(.txt files),
})
  • Run this command to evaluate the model. We are using the classification accuracy as our evaluation metric.
# Evaluate our model in the test set
python run.py -m eval --dataset_type=test --trained_weights=weights/glamor-net/Model

Training

Firstly please extract the faces from train set (val set is optional)

  • Specify the path to the dataset in config.py (train_images, val_images, test_images)
  • Specify the desired face-extracted output path in config.py (train_crop, val_crop, test_crop)
config = config.copy({

    'train_images': 'path_to_training_images',
    'train_crop':   'path_to_training_cropped_faces' #(.txt files),

    'val_images': 'path_to_validation_images',
    'val_crop':   'path_to_validation_cropped_faces' #(.txt files)

})
  • Perform face extraction on both dataset_type by running the commands:
python run.py -m extract --dataset_type=<train, val or test>

Start training:

# Train a new model from sratch
python run.py -m train 

# Continue training a model that you had trained earlier
python run.py -m train --resume=path/to/trained_weights

# Resume the last checkpoint model
python run.py -m train --resume=last

Prediction

We support prediction on single image or on images in a directory by running this command:

# Predict on single image
python predict.py --trained_weights=weights/glamor-net/Model --input=test_images/1.jpg --output=path/to/out/directory

# Predict on images in directory
python predict.py --trained_weights=weights/glamor-net/Model --input=test_images/ --output=out/

Use the help option to see a description of all available command line arguments

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Comments
  • About Training

    About Training

    Hello, are the hyperparameters used when the two branches are separately trained separately? Is it the same as the hyperparameters during joint training? thanks

    I also want to ask another question. I think the author has referred to another similar paper in the article[32]. Do you have the source code of this paper?

    opened by TheKOne 0
Owner
Minh Nhat Le
Hi
Minh Nhat Le
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