Update README.md

README.md

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----
-license: mit
----
+---
+pretty_name: Cats vs Dogs Classifier
+tags:
+- image-classification
+- tensorflow
+- keras
+- efficientnet
+- gradio
+widget:
+- src: >-
+    https://raw.githubusercontent.com/huggingface/datasets-server/main/assets/image-classification/train/image.jpg
+  example_title: Dog example
+- src: >-
+    https://raw.githubusercontent.com/huggingface/datasets-server/main/assets/image-classification/test/image.jpg
+  example_title: Cat example
+license: mit
+metrics:
+- accuracy
+base_model:
+- google/efficientnet-b0
+library_name: keras
+---
+
+# Cats vs Dogs Classifier with EfficientNetB0
+
+This model is a deep learning classifier capable of distinguishing between images of cats and dogs. It leverages the power of `EfficientNetB0` for robust feature extraction and is fine-tuned for this binary classification task.
+
+## Dataset
+
+- **Name**: `cats_vs_dogs` from `tensorflow_datasets`
+- **Description**: Contains 23,262 images in total, split into approximately equal numbers of cat and dog images.
+  - Cats are labeled as `0`.
+  - Dogs are labeled as `1`.
+
+## Model Architecture
+
+The model is built using a pre-trained `EfficientNetB0` backbone (pre-trained on ImageNet) and custom classification layers:
+
+- **Base Model**: `EfficientNetB0` (without top layers, `include_top=False`)
+- **Input Shape**: `(150, 150, 3)` (images are resized to this dimension)
+- **Custom Layers**:
+  - `GlobalAveragePooling2D()`: Reduces spatial dimensions of feature maps.
+  - `Dense(units=100, activation="relu")`: A fully connected layer with ReLU activation.
+  - `Dense(units=1, activation="sigmoid")`: Output layer for binary classification, producing a probability between 0 and 1.
+
+## Training Details
+
+- **Epochs**: 7 (with EarlyStopping)
+- **Batch Size**: 32
+- **Optimizer**: Adam with a learning rate of 5e-5
+- **Loss Function**: Binary Crossentropy
+- **Callbacks**: EarlyStopping (patience=3, monitor='val_loss'), ModelCheckpoint (saves best model based on 'val_loss')
+
+## Evaluation Metrics
+
+The model was evaluated on a test set of 4653 images. Here are the key metrics:
+
+- **Test Loss**: 0.0334
+- **Test Accuracy**: 0.9884
+
+**Classification Report**:
+```
+              precision    recall  f1-score   support
+
+           0       0.99      0.99      0.99      2273
+           1       0.99      0.99      0.99      2380
+
+    accuracy                           0.99      4653
+   macro avg       0.99      0.99      0.99      4653
+weighted avg       0.99      0.99      0.99      4653
+```
+
+## How to Use
+
+You can use this model for image classification. Here's a Python snippet using TensorFlow and the `huggingface_hub` library:
+
+```python
+import tensorflow as tf
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+# Download the model from Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="sirunchained/cats-vs-dogs",
+    filename="cats-vs-dogs.keras"
+)
+
+# Load the model
+model = tf.keras.models.load_model(model_path)
+
+classes = ["Cat", "Dog"]
+IMG_SIZE = 150
+
+def predict_image(image_path):
+    img = Image.open(image_path).resize((IMG_SIZE, IMG_SIZE))
+    img_array = np.array(img)
+    img_array = tf.expand_dims(img_array, axis=0)
+    img_array = tf.cast(img_array, tf.float32)
+
+    predictions = model.predict(img_array)
+    predicted_confidence = predictions[0][0] # Since it's a binary classifier with sigmoid output
+
+    if predicted_confidence >= 0.5:
+        predicted_class = "Dog"
+        confidence = predicted_confidence
+    else:
+        predicted_class = "Cat"
+        confidence = 1 - predicted_confidence
+
+    return predicted_class, confidence
+
+# Example usage:
+# predicted_class, confidence = predict_image("path/to/your/image.jpg")
+# print(f"Predicted: {predicted_class} with confidence: {confidence:.2f}")
+```
+
+## Gradio Demo
+
+You can interact with a live demo of this model through Gradio:
+
+[Gradio Demo Link](https://huggingface.co/spaces/sirunchained/cats-vs-dogs-gradio)