Learnable Adaptive Cosine Estimator (LACE) for Image Classification:

Learnable Adaptive Cosine Estimator (LACE) for Image Classification

Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart, and Alina Zare

Note: If this code is used, cite it: Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart, and Alina Zare. (2021, October 15). GatorSense/LACE: Initial Release (Version v1.0). Zendo. https://doi.org/10.5281/zenodo.5572704

IEEE Xplore

WACV Repository

arXiv

BibTeX

In this repository, we provide the paper and code for the Learnable Adaptive Cosine Estimator (LACE) approach in "Learnable Adaptive Cosine Estimator (LACE) for Image Classification."

Installation Prerequisites

This code uses python, pytorch, and barbar. Please use Pytorch's website to download necessary packages. Barbar is used to show the progress of model. Please follow the instructions here to download the module.

Demo

Run demo.py in Python IDE (e.g., Spyder) or command line.

Main Functions

The Learnable Adaptive Cosine Estimator (LACE) runs using the following functions.

1. Intialize model

model, input_size = intialize_model(**Parameters)

2. Prepare dataset(s) for model

dataloaders_dict = Prepare_Dataloaders(**Parameters)

3. Train model

train_dict = train_model(**Parameters)

4. Test model

test_dict = test_model(**Parameters)

Parameters

The parameters can be set in the following script:

Demo_Parameters.py

Inventory

https://github.com/GatorSense/LACE

└── root dir
    ├── demo.py   //Run this. Main demo file.
    ├── Demo_Parameters.py // Parameters file for demo.
    ├── Prepare_Data.py  // Load data for demo file.
    └── Utils  //utility functions
        ├── Embedding_Model.py  // Generates model with an encoder following the final layer (if necessary). 
        ├── loss_functions.py  // Contains LACE, angular softmax, and feature regularization methods for models.
        ├── Loss_Model.py  // Creates model with backbone and regularization loss.
        ├── Generating_Learning_Curves.py  // Plot training and validation accuracy and error measures.
        ├── Generate_TSNE_visual.py  // Create TSNE visual for results.
        ├── Network_functions.py  // Contains functions to initialize, train, and test model. 
        ├── pytorchtools.py // Function for early stopping.
        ├── Save_Results.py  // Save results from demo script.

License

This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.

This product is Copyright (c) 2021 J. Peeples, C. McCurley, S. Walker, D. Stewart, and A. Zare. All rights reserved.

Citing LACE

If you use the LACE code, please cite the following reference using the following entry.

Plain Text:

J. Peeples, C. McCurley, S. Walker, D. Stewart, and A. Zare, "Learnable Adaptive Cosine Estimator (LACE) for Image Classification," In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 3479-3489, In Press.

BibTex:

@InProceedings{Peeples_2022_WACV,
  title = {Learnable Adaptive Cosine Estimator (LACE) for Image Classification},
  author = {Peeples, Joshua and McCurley, Connor and Walker, Sarah, and Stewart, Dylan, and Zare, Alina},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month = {January},
  year = {2022},
  pages = {3479-3489}
}