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aqillakhamis/
cat-dog-beginner-classifier

Name: cat-dog-beginner-classifier
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CatDog Beginner Classifier

CatDog Beginner Classifier is an image-classification model that predicts whether an input image contains a domestic cat or a domestic dog.

The model was created for educational demonstrations and beginner AI projects.

Model Details

Model name: CatDog Beginner Classifier
Model version: 1.0
Model type: Image classification
Architecture: Convolutional Neural Network
Framework: TensorFlow and Keras
Developed by: Replace with your name or group name
Release date: July 2026

Model Inputs and Outputs

Input

RGB image
Image size: 224 × 224 pixels
Supported formats: JPG, JPEG and PNG
Pixel values after normalization: 0–1

Output

The model returns a probability for each class:

Training Data

Total approved images: 2,000
Cat images: 1,000
Dog images: 1,000
Dataset version: 1.0
Annotation guideline version: 1.0

Dataset Split

Split	Images	Percentage
Training	1,400	70%
Validation	300	15%
Testing	300	15%

Training Procedure

Preprocessing

Convert images to RGB.
Resize images to 224 × 224 pixels.
Normalize pixel values to 0–1.
Remove corrupted images.

Hyperparameters

Parameter	Value
Epochs	20
Batch size	32
Optimizer	Adam
Learning rate	0.001
Loss function	Binary cross-entropy

Evaluation

The model was evaluated using 300 test images that were not used during model training.

Metric	Result
Accuracy	91.0%
Precision	90.0%
Recall	89.0%
F1-score	89.5%

Workshop note: Replace these example values with the actual results from your model.

Intended Use

The model may be used for:

Beginner image-classification exercises
Teaching AI model evaluation
Demonstrating Model Cards
Educational applications

Intended Users

Students
Lecturers
Beginner AI developers

Out-of-Scope Use

The model should not be used for:

Veterinary diagnosis
Animal-health decisions
Animal-breed identification
Wildlife classification
Safety-critical decisions
Commercial applications without further evaluation

Limitations

The model may produce incorrect predictions when:

Images are dark or blurry.
The animal is partly hidden.
The animal is very small.
Multiple animals appear in the image.
The image contains a cartoon or toy.
The input differs significantly from the training data.

Bias and Risks

The dataset may overrepresent daylight images, common animal breeds, indoor environments and certain image sources.

A high confidence score does not guarantee that a prediction is correct.

Deployment Recommendations

Apply the documented preprocessing.
Display prediction probabilities.
Mark low-confidence predictions as uncertain.
Allow human review for important decisions.
Record incorrect predictions.
Monitor model performance.
Retrain the model when the data changes.

Maintenance

Model owner: Replace with your name or group name
Review frequency: Every six months
Retraining trigger: Significant performance decline

Version History

Version	Date	Description
1.0	July 2026	Initial model release

aqillakhamis/cat-dog-beginner-classifier