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116 lines
3.0 KiB
Markdown
116 lines
3.0 KiB
Markdown
# Vision Transformers for Face Recognition
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This is the official implementation of the paper titled "Comprehensive Comparison of Vision Transformers and
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Traditional Convolutional Neural Networks for Face Recognition Tasks".
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The whole project, including model weights and extensive results can be found in
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https://www.gti.ssr.upm.es/data as a .zip file.
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The structure of directories should look like:
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```
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Project
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├── datasets
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| ├── LFW
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| | └── lfw
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| ├── UPM-GTI-Face
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| └── VGG-Face2
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└── saved_results
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├── Models
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| ├── ResNet_50
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| ├── VGG_16
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| └── ViT_B32
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└── Tests
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├── LFW
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└── UPM-GTI-Face
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```
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## Pre-requisites
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1. Download the following datasets and move them to their respective directories:
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* [VGG-Face2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/)
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* [Labeled Faces in the Wild (LFW)](http://vis-www.cs.umass.edu/lfw/#download)
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* [UPM-GTI-Face](https://www.gti.ssr.upm.es/data/upm-gti-face-dataset)
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```sh
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cd ~/Downloads/
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mv VGG-Face2/* ~/Project/datasets/VGG-Face2/
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mv lfw/* ~/Project/datasets/LFW/lfw/
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mv UPM-GTI-Face/* ~/Project/datasets/UPM-GTI-Face
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```
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2. Install the requirements
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```bash
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pip install -r requirements.txt
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```
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## Training
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The training of the three models can be achieved executing their respective files.
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The results of the training will be saved to ``/tmp`` directory.
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* ViT_B32
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```bash
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python vitb32_train.py
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```
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* ResNet_50
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```bash
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python resnet50_train.py
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```
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* VGG_16
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```bash
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python vgg16_train.py
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```
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Any of the networks can be trained from scratch by commenting the following line in the respective training file:
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```python
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"""
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LOAD PRE-TRAINED MODEL WEIGHTS
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"""
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# Load pre-trained model weights before training
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best_weights = "./saved_results/Models/ViT_B32/checkpoint"
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vit_model.load_weights(best_weights)
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```
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## LFW Test
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The test can be performed by executing the corresponding file. Results will be saved to
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``/saved_results/Tests/LFW``.
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```bash
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python lfw_test.py
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```
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## UPM-GTI-Face Test
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The test can be performed by executing the corresponding file. Results will be saved to
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``/saved_results/Tests/UPM-GTI-Face``.
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```bash
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python upm-gti-face_test.py
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```
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## Authors
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* Marcos Rodrigo - marcos.rodrigo@upm.es
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* Carlos Cuevas - carlos.cuevas@upm.es
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* Narciso García - narciso.garcia@upm.es
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## Citation
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```BibTex
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@inproceedings{rodrigo2023comprehensive,\
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title={Comprehensive Comparison of Vision Transformers and Traditional Convolutional Neural Networks for Face Recognition Tasks},\
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author={Rodrigo, Marcos and Cuevas, Carlos and García, Narciso},\
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booktitle={Under revision for the 2023 IEEE International Conference on Image Processing},\
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year={2023},\
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organization={IEEE}\
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}
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``` |