README: Swin-V2-Base for AquaMonitor JYU Dataset

Overview

This project focuses on the automatic classification of aquatic macroinvertebrates using deep learning to enhance environmental biomonitoring. Two neural network architectures were used: Swin-V2-Base and ResNet18. Swin-V2-Base achieved a 77% weighted F1-score. This is an academic project aimed at evaluating the effectiveness of modern models in macroinvertebrate classification.

Project Goal

Accurate monitoring of aquatic macroinvertebrates is crucial for assessing water quality and biodiversity. Manual identification requires significant time and specialized knowledge, making it difficult to scale. Deep learning can automate this process, improving both speed and accuracy.

Importance and Relevance

• Ecological Context: Insect biomass has declined by 75% over the past 30 years, making monitoring more crucial than ever.
• Challenges of Manual Identification: High costs, a limited number of experts, and scalability issues.
• Deep Learning Advancements: Modern models achieve expert-level accuracy under laboratory conditions.

Dataset Description

AquaMonitor JYU is a subset of the large AquaMonitor dataset, which contains images of aquatic macroinvertebrates.

• Number of Classes: 31
• Training Set: 40,880 images (from 1,049 individuals)
• Validation Set: 6,394 images (from 157 individuals)
• Test Set: Hidden
• Image Format: 256x256

Class Examples

To better understand the task's complexity, below are sample images from all 31 classes:

Class Imbalance

The dataset exhibits significant class imbalance, with the number of images per class ranging from 400 to 3,500. This poses challenges during model training, as rare classes may lack sufficient representation for effective generalization.

Below is a histogram showing the distribution of classes in the training set:

Swin-V2-Base Architecture

Swin-V2-Base is a transformer-based architecture that utilizes hierarchical representation and local windows, allowing it to efficiently process high-resolution images. However, the model showed signs of overfitting, emphasizing the importance of pretraining and regularization techniques.

Model Configuration

• Image Size: 256x256
• Number of Classes: 31
• Optimizer: AdamW
• Loss Function: CrossEntropyLoss with label smoothing = 0.1
• Epochs: 6
• Batch Size: 64
• Max Learning Rate: 5e-5
• Regularization: Drop Path Rate = 0.2

Weight Freezing

To improve model training:

• Patch embedding layer weights were frozen
• Parameters of the first two layers were frozen

Training Setup

• OneCycleLR was used for adaptive learning rate scheduling.
• Model was trained on A100 GPU in Google Colab with FP16 mixed precision.
• Gradient scaling was performed using torch.amp.GradScaler.

Model download

The Swin-V2-Base model can be downloaded at the following link: Download model.pt

Data Augmentation

Two augmentation strategies were applied:

1. Standard augmentation for all images:
   • Random rotation (10°)
   • Color jitter (brightness, contrast, saturation, hue)
   • Random resized cropping
   • Gaussian blur
   • Random affine transformations
2. Stronger augmentation for rare classes:
   • Random horizontal flip
   • Random perspective distortion
   • Stronger brightness and contrast adjustments

Model Files

The repository contains:

• model.pt – The trained model checkpoint
• model.py – The model class for loading the trained model

Results

Architecture	Weighted F1-score
Swin-V2-Base	77%
ResNet18	74%

The Swin-V2-Base model exhibited overfitting tendencies, whereas ResNet18 had lower performance overall.

Conclusions

• Swin-V2-Base achieved a 77% weighted F1-score, but overfitting was observed.
• Transfer learning is crucial, as using pretrained models significantly improves results.
• Possible improvements: Increasing generalization by data augmentation, regularization, and alternative training strategies.

License

This project is an academic study and is intended for research purposes only.

Deep_Learning-Classification-Swin-v2-B