nipunsgeeth/cats-vs-dogs-cnn

📄 README.md (Model Card) for your Cats‑vs‑Dogs model

# Cats vs Dogs: CNN / Transfer‑Learning Model

This repository contains a convolutional neural network model trained to classify images of cats and dogs. The model was built using TensorFlow / Keras, trained on the `cats_vs_dogs` dataset, and designed for binary image classification.

## ✅ Model Details

- **Model type:** CNN (Transfer learning + custom head)  
- **Input shape:** 160 × 160 × 3 (RGB image)  
- **Output:** Single sigmoid output — probability that the image is a *dog*.  
- **Training data:** cats_vs_dogs (loaded via `tensorflow_datasets`)  
- **Preprocessing:** Images resized to 160×160, pixel values normalized (0–1)  
- **Training & validation split:** 80% train / 20% validation  
- **Library:** TensorFlow / Keras  

## 🎯 Intended use

Use this model to classify images into two categories: **cat** or **dog**. You can use it for:

- Quick inference on image files  
- As a baseline or demo for image classification tasks  
- Educational purposes — to understand how CNN + transfer learning works  

## ⚠️ Limitations

- The model performs reasonably but is **not state-of-the-art**; it may misclassify images with unusual angles, background clutter, or partial visibility.  
- Because the dataset used has limited diversity, the model might be biased toward “typical” cat/dog images (good lighting, clear view). Use caution if applying to real-world images.  
- **Not recommended for critical use** (e.g., medical, legal, safety-critical systems) — this is an example model.

## 🧰 How to Use (Inference Example)

```python
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image

# Load model
model = tf.keras.models.load_model("path/to/downloaded_model/")

# Load and preprocess image
img = image.load_img("path/to/your_image.jpg", target_size=(160,160))
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0)

# Predict
prob = model.predict(img)[0][0]
if prob >= 0.5:
    print("Dog 🐶 — probability:", prob)
else:
    print("Cat 🐱 — probability:", 1-prob)

📦 Files

• saved_model/ or .keras / .h5 — the trained model files
• (Optional) example_usage.py — a script to load the model and run predictions

📈 Training & Evaluation Summary

Metric	Value
Validation Accuracy (after fine‑tuning)	~ 0.5098
Loss (binary cross‑entropy)	…0.6931

(If you trained longer / fine‑tuned — update these accordingly.)

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👨‍💻 Contributing / Retraining

If you want to improve this model:

1. Retrain or fine‑tune with more data or stronger augmentation
2. Use larger / more powerful backbone (e.g. ResNet, EfficientNet)
3. Evaluate on a wider test set for robustness

Feel free to fork the repository, retrain, and open a pull request.