Wildlife Monitoring Dashboard — Nationaal Park De Hoge Veluwe

A real-time wildlife camera-trap monitoring dashboard powered by an EfficientNet V2-S classifier and multiple Explainable AI (XAI) methods. Built for the 0HM340 Human-AI Interaction assignment.

The dashboard simulates a live feed of camera-trap detections across five locations in Nationaal Park De Hoge Veluwe, classifying images into seven species (bear, deer, fox, hare, moose, person, wolf) and providing interactive explanations of each prediction.

Prerequisites

Python 3.13+
uv package manager
CUDA GPU (optional, falls back to CPU)

Quick Start

1. Clone the repository

git clone <repo-url>
cd assignment-HAI

2. Obtain the model weights

The trained model weights are not included in the repository due to their size. Place the file in the project root:

File	Size	Description
`efficientnet_v2_wild_forest_animals.pt`	~78 MB	Fine-tuned EfficientNet V2-S weights

The model was fine-tuned in final.ipynb.

The dataset (wild-forest-animals-and-person-1/) is downloaded automatically from Roboflow on first launch if not already present on disk.

3. Install dependencies

uv sync

4. Run the dashboard

uv run python dashboard.py

The server starts at http://localhost:5000.

Project Structure

assignment-HAI/
├── dashboard.py          # Flask application (main entry point)
├── final.ipynb           # Training, evaluation, and XAI notebook
├── map.webp              # Park map background image
├── pyproject.toml        # Project metadata and dependencies
├── uv.lock               # Locked dependency versions
├── .python-version       # Python version pin (3.13)
├── .gitignore
├── README.md             # This file
├── USER_GUIDE.md         # End-user documentation
├── DEVELOPER_GUIDE.md    # Developer documentation
├── efficientnet_v2_wild_forest_animals.pt  # Model weights (not in git)
└── wild-forest-animals-and-person-1/       # Dataset (not in git)
    ├── train/
    ├── test/
    └── valid/

XAI Methods

Method	What it shows
ScoreCAM	Gradient-free saliency heatmap highlighting regions the model attends to
LIME	Superpixel-based explanations for the top-2 predicted classes
Contrastive LIME	Highlights which regions distinguish the top-1 prediction from the top-2
Nearest Neighbours	Three training images most similar in feature space (cosine similarity)

License

Academic project — 0HM340 Human-AI Interaction, TU/e.