---
license: mit
language:
- uk
metrics:
- accuracy
base_model:
- google/efficientnet-b0
pipeline_tag: image-classification
tags:
- computer-vision
- cats
- efficientnet
- student-project
---


# CatGuard EfficientNet

## Overview

CatGuard EfficientNet is an image classification model designed to identify a specific domestic cat named **Syrnyn** from photographs.
The model was developed as a first-year university computer vision project and serves as the first step toward a future IoT monitoring system capable of detecting when a specific cat enters a restricted area.

---

## Problem

One of the household cats frequently enters the kitchen and attempts to eat food left unattended.
Monitoring this behavior manually is inconvenient and inconsistent. The goal of this project is to automatically recognize the target cat from camera images.

---

## Task

Binary image classification.

Input:

- Image containing a cat

Output:

- Naughty cat
- Other Cat

---

## Dataset

Custom dataset collected from personal photographs.

Classes:

- Naught Cat (black cat)
- Other Cat

Dataset split:

- Train: 70%
- Validation: 15%
- Test: 15%

---

## Model Architecture

The project uses a two-stage pipeline:

```text
Image
 ↓
DETR Object Detector
 ↓
Cat Detection
 ↓
EfficientNet-B0
 ↓
Classification Head
 ↓
Naughty Cat / Other Cat
```

### Stage 1: Object Detection

The system first uses **DETR (DEtection TRansformer)** (`facebook/detr-resnet-50`) to determine whether a cat is present in the image.

Possible outcomes:

- Cat detected → continue to classification
- No cat detected → return a warning message

### Stage 2: Cat Classification

If a cat is detected, the image is passed to an **EfficientNet-B0** classifier trained using transfer learning.

The classifier predicts one of two classes:

- Naughty Cat
- Other Cat
---

## Results

Best validation accuracy:

**89.7%**

The model correctly identifies the target cat in approximately 9 out of 10 validation images.

---

## Future Work

Current version:

```text
Image
 ↓
Cat Classification
```

Planned extension:

```text
Camera
 ↓
Cat Detection
 ↓
Cat Classification
 ↓
IoT Device Response
```

The future system may automatically detect the target cat entering the kitchen and trigger a connected IoT device.

---

## Author

Sandra Korol
Computer Vision Project