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:
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:
Image
↓
Cat Classification
Planned extension:
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