cat-dog-beginner-classifier

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:

• Cat
• Dog

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

Training Procedure

Preprocessing

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

Hyperparameters

Evaluation

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

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

1. Apply the documented preprocessing.
2. Display prediction probabilities.
3. Mark low-confidence predictions as uncertain.
4. Allow human review for important decisions.
5. Record incorrect predictions.
6. Monitor model performance.
7. 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