Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# LARS-MobileNet-V4
+
+This repository contains the implementation of the lightweight convolutional neural network architecture described in the paper "Advancing Real-Time Crop Disease Detection on Edge Computing Devices using Lightweight Convolutional Neural Networks."
+
+## Overview
+
+This project introduces LARS-MobileNetV4, an optimized version of MobileNetV4 specifically designed for real-time crop disease detection on resource-constrained edge devices such as Raspberry Pi. Our implementation achieves 97.84% accuracy on the Paddy Doctor dataset while maintaining fast inference times (88.91ms on Raspberry Pi 5), making it suitable for deployment in agricultural field settings.
+
+## Key Features
+
+- **Optimized MobileNetV4 Architecture**: Enhanced with Squeeze-and-Excitation (SE) blocks and Efficient Channel Attention (ECA) mechanisms
+- **Resource-Efficient Design**: Significantly reduced model size (10.2MB) compared to ResNet34 (85.3MB)
+- **Real-time Performance**: Average inference time of 39ms on CPU and 88.91ms on Raspberry Pi 5
+- **High Accuracy**: 97.84% detection accuracy across 12 common rice diseases
+- **Custom Loss Function**: Combination of Focal Loss and Label Smoothing for better handling of class imbalance
+- **Comprehensive Data Augmentation**: Robust augmentation pipeline to improve model generalization
+- **Deployment-Ready**: Optimized for TFLite deployment on edge devices
+
+## Model Architecture
+
+LARS-MobileNetV4 builds upon the recently introduced MobileNetV4 architecture with several key optimizations:
+
+1. **Universal Inverted Bottleneck (UIB)**: Merges features of Inverted Bottlenecks, ConvNext, and Feed Forward Networks to enhance flexibility in spatial and channel mixing
+2. **Mobile Multi-Query Attention (MQA)**: An accelerator-optimized attention mechanism that reduces memory bandwidth bottlenecks
+3. **Squeeze-and-Excitation Blocks**: Added to adaptively recalibrate channel-wise feature responses
+4. **Efficient Channel Attention**: Captures cross-channel interactions with minimal computational overhead
+5. **Neural Architecture Search (NAS)**: Tailored architecture for specific hardware
+
+## Performance Comparison
+
+| Model                 | Parameters (M) | Accuracy (%) | Model Size (MB) | Inference Time on CPU (ms) | Inference Time on Raspberry Pi 5 (ms) |
+| --------------------- | -------------- | ------------ | --------------- | -------------------------- | ------------------------------------- |
+| ResNet34              | 21.79          | 97.50        | 85.3            | 148.93                     | 264.50                                |
+| MobileNet-V2          | 3.5            | 92.42        | 9.2             | 40.00                      | 73.09                                 |
+| MobileNet-V3          | 2.5            | 95.62        | 10.3            | N/A                        | N/A                                   |
+| MobileNet-V4          | 3.8            | 97.17        | 10.2            | 39.20                      | 88.91                                 |
+| **LARS-MobileNet-V4** | **3.8**        | **97.84**    | **10.2**        | **39.20**                  | **88.91**                             |
+
+## Training Strategies
+
+Our implementation includes several optimization techniques:
+
+| Model Variation                                                                  | Train Accuracy (%) | Test Accuracy (%) |
+| -------------------------------------------------------------------------------- | ------------------ | ----------------- |
+| MobileNet-V4 Baseline                                                            | 99.93              | 97.17             |
+| MobileNet-V4 (Augmentations)                                                     | 99.60              | 97.21             |
+| MobileNet-V4 (FocalLabelSmoothingLoss)                                           | 99.71              | 97.79             |
+| MobileNet-V4 (Augmentations, FocalLabelSmoothingLoss, Squeeze-Excitation Blocks) | 99.68              | **97.84**         |
+
+### Custom Loss Function
+
+We implement a combination of Focal Loss and Label Smoothing:
+
+1. **Label Smoothing**: Redistributes confidence across classes
+
+   $$y_{smooth} = (1 - ε)y + ε/C$$
+
+   where ε is the smoothing factor and C is the total number of classes.
+
+2. **Focal Loss**: Focuses on harder examples
+
+   $$L_{focal}(pt) = -α(1 - pt)^γ log(pt)$$
+
+   where pt is the predicted probability for the true class.
+
+3. **Combined Loss (FLS)**:
+   
+   $$L_{FLS} = -α(1 - pt)^γ log(p_{smooth})$$
+
+## Requirements
+
+```
+torch
+torchvision
+timm
+numpy
+pandas
+Pillow
+scikit-learn
+tqdm
+wandb
+```
+
+### Data Preparation
+
+Organize your data as follows:
+
+```
+├── train_images/
+│   ├── disease_class_1/
+│   │   ├── image1.jpg
+│   │   ├── image2.jpg
+│   │   └── ...
+│   ├── disease_class_2/
+│   └── ...
+├── test_images/
+└── train.csv
+```
+
+The train.csv file should contain:
+
+- `image_id`: Filename of the image
+- `label`: Disease class name
+
+### Configuration
+
+Key hyperparameters can be modified at the top of the script:
+
+```python
+LEARNING_RATE = 0.0001
+ARCHITECTURE = "MobileNetV4"
+EPOCHS = 50
+BATCH_SIZE = 64
+OPTIMISER = "Adam"
+LOSS_FUNCTION = "FocalLabelSmoothingComboLoss"
+NUM_CLASSES = 13  # 12 disease classes + 1 normal class
+PRETRAINED = True
+```
+
+## Citation
+
+If you use this code in your research, please cite our paper:
+
+```
+@article{Nanda, T.R., Shukla, A., Srinivasa, T.R., Bhargava, J., Chauhan, S. (2025).
+Advancing Real-Time Crop Disease Detection on Edge Computing Devices Using Lightweight Convolutional Neural Networks.
+In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2025. Lecture Notes in Networks and Systems,
+vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-032-00071-2_33
+}
+```
+
+## Acknowledgements
+
+- We use the Paddy Doctor dataset for training and evaluation [Petchiammal et al., 2022]