README.md

---
license: mit
datasets:
  - microsoft/cats_vs_dogs
metrics:
  - accuracy
tags:
  - image-classification
  - tensorflow
  - cnn
  - cats-vs-dogs
  - computer-vision
---

# 🐱🐶 Cat vs Dog Classifier (TensorFlow CNN)

A Convolutional Neural Network (CNN) model trained to classify images of **cats** and **dogs** using the [microsoft/cats_vs_dogs](https://huggingface.co/datasets/microsoft/cats_vs_dogs) dataset. Built using TensorFlow and trained on a balanced dataset of 23,000+ images.

---

## 🧠 Model Details

| Field              | Details                                  |
|-------------------|------------------------------------------|
| **Architecture**  | CNN (3 Conv layers + Dense + Dropout)    |
| **Framework**     | TensorFlow / Keras                       |
| **Input Shape**   | 224 × 224 × 3 (RGB)                      |
| **Output**        | 2 classes: `Cat (0)`, `Dog (1)`          |
| **Loss Function** | Sparse Categorical Crossentropy          |
| **Optimizer**     | Adam                                     |
| **Dataset**       | microsoft/cats_vs_dogs (Hugging Face)   |
| **Training Size** | ~18.7k images (80% split)                |
| **Validation**    | ~4.7k images (20% split)                 |

---

## 🧪 Performance

| Metric     | Value    |
|------------|----------|
| Accuracy   | ~95%     |
| Confidence | Softmax output used in predictions |

> Evaluation done using 20% validation split.

---

## 🔍 How to Use

```python
from huggingface_hub import from_pretrained_keras
import tensorflow as tf
import numpy as np
from PIL import Image

# Load the model
model = from_pretrained_keras("UsamaHF/Cat-dog-classification")

# Load and preprocess image
img = Image.open("example.jpg").resize((224, 224)).convert("RGB")
img_array = np.expand_dims(np.array(img).astype("float32") / 255.0, axis=0)

# Get inference function
infer = model.signatures["serving_default"]

# Predict
output = infer(tf.constant(img_array))
predictions = output["output_0"].numpy()  # Replace "dense_1" if needed

predicted_class = np.argmax(predictions[0])
print("Predicted:", "Dog" if predicted_class == 1 else "Cat")