Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis:

Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis

Akshatha Mohan and Joshua Peeples

Note: If this code is used, cite it: Akshatha Mohan and Joshua Peeples.

Zendo. []

IEEE Xplore (CVPRW)

arXiv

BibTeX

In this repository, we provide the paper and code for the "Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis."

Installation Prerequisites

This code uses python and necessary packages in pytorch Please use Pytorch's website to download necessary packages.

Demo

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

Main Functions

The Lacunarity Pooling layer 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/Advanced-Vision-and-Learning-Lab/V4A_Lacunarity_Pooling_Layer

└── root dir
	├── demo.py   //Run this. Main demo file.
	├── Demo_Parameters.py // Parameters file for demo.
	├── Prepare_Data.py  // Load data for demo file.
	├── View_Results.py // Run this after demo to view saved results.
    	├── Datasets
		├── Get_transform.py // Transforms applied on test, train, val dataset splits
		├── Pytorch_Datasets.py // Return Index for Pytorch datasets
		├── Pytorch_Datasets_Names.py // Return names of classes in each dataset
		├── Split_Data.py // Returns data splits for train, test and validation
	└── Utils  //utility functions
		├── Compute_FDR.py  // Compute Fisher Score
		├── Confusion_mats.py  // Create and plot confusion matrix.
		├── 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.
		├── CustomNN.py // Contains the shallow neural network and densenet161 model implementation
		├── Timm_Models.py // Contains the timm models implementation
		├── Computer_sizes.py // Function for computing features for fully connected layer
		├── DBC.py // Compute Differential BoX Counting pooling layer
		├── Multi_Scale_Lacunarity.py // Compute Multi-scale pooling layer
		├── Base_Lacunarity.py // Compute Base lacunarity pooling layer
		├── Lacunarity_Pooling.py // Contains fusion pooling approach for all lacunarity layer
		├── Fractal_Pooling.py // Contains fractal pooling approach
		├── xai_methods.py //Function to visualize EigenCAM on feature maps
    	├── 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) 2023 A. Mohan and J. Peeples. All rights reserved.

Citing Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis

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

Plain Text:

A. Mohan and J. Peeples, “Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, in Press.

BibTex:

@inproceedings{mohan2024lacunarity,
  title={Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis},
  author={Mohan, Akshatha and Peeples, Joshua},
  booktitle={2024 Agriculture-vision IEEE/CVF CVPR 2024},
  pages={950-953},
  year={2024},
  organization={CVPR}
}