Update README.md

README.md

@@ -11,4 +11,164 @@ license: mit
 short_description: Cat vs Dog classifier with InceptionV3 model.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+[If you would like a detailed explanation of this project, please refer to the Medium article below.](https://medium.com/@ai.omar.rehan/building-a-near-perfect-cat-vs-dog-classifier-with-inceptionv3-01a5f9730907)
+
+---
+
+# Cats vs Dogs Image Classification (InceptionV3 + TensorFlow)
+
+## Project Overview
+
+This project focuses on building an image classification model that can distinguish between **cats and dogs** using **Transfer Learning** with the InceptionV3 architecture.
+
+Instead of training a deep learning model from scratch, this project uses a pre-trained model and adapts it to solve a binary classification problem efficiently.
+
+The goal of this project is to practice building a real-world computer vision pipeline including data preprocessing, training, evaluation, and visualization.
+
+---
+
+## Dataset
+
+The project uses the **Cats and Dogs dataset**, which contains around:
+
+* ~6,000 cat images
+* ~6,000 dog images
+
+The dataset is balanced, which helps the model learn both classes fairly and avoids bias toward one class.
+
+---
+
+## Data Preprocessing
+
+Before training, images go through several preprocessing steps:
+
+* Resize images to **256 × 256**
+* Normalize pixel values
+* Handle very bright or very dark images
+* Apply data augmentation to improve generalization:
+
+  * Random flipping
+  * Random brightness changes
+  * Random contrast changes
+
+TensorFlow’s `tf.data` pipeline is used to efficiently load and prepare data.
+
+---
+
+## Model Architecture
+
+This project uses **Transfer Learning with InceptionV3**.
+
+### Base Model
+
+* Pre-trained on ImageNet
+* Used as a feature extractor
+* Frozen during initial training
+
+### Custom Classification Head
+
+Added on top of the base model:
+
+* Global Average Pooling
+* Dense layer (512 neurons, ReLU)
+* Dropout (0.5) to reduce overfitting
+* Final Dense layer with **Sigmoid** activation for binary classification
+
+---
+
+## Training Strategy
+
+### Optimizer
+
+* Adam optimizer
+
+### Loss Function
+
+* Binary Cross-Entropy
+
+### Training Enhancements
+
+The project uses callbacks to improve training:
+
+* **EarlyStopping**
+
+  * Stops training when validation stops improving
+* **ModelCheckpoint**
+
+  * Saves the best model automatically
+* **ReduceLROnPlateau**
+
+  * Reduces learning rate when progress slows down
+
+---
+
+## Results & Evaluation
+
+Model performance was evaluated using several visualization techniques.
+
+---
+
+### Accuracy
+
+```
+76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.9941 - loss: 0.0194
+Test Accuracy: 0.9933
+76/76 ━━━━━━━━━━━━━━━━━━━━ 16s 115ms/step
+Precision: 0.2498, Recall: 0.5000, F1-score: 0.3331
+
+Classification Report:
+              precision    recall  f1-score   support
+
+        cats       0.50      1.00      0.67       601
+        dogs       0.00      0.00      0.00       602
+
+    accuracy                           0.50      1203
+   macro avg       0.25      0.50      0.33      1203
+weighted avg       0.25      0.50      0.33      1203
+```
+---
+![Metrics](https://files.catbox.moe/smuda2.png)
+**Observation:**
+Training and validation accuracy both increase steadily and reach high performance (~98–99%).
+
+---
+
+### Loss
+
+![Metrics](https://files.catbox.moe/avxel3.png)
+
+**Observation:**
+Both training and validation loss decrease and stabilize, indicating good learning and low overfitting.
+
+---
+
+### Confusion Matrix
+
+![CM](https://files.catbox.moe/ehuyhi.png)
+
+**Observation:**
+Most predictions lie along the diagonal, meaning the model correctly classifies both cats and dogs most of the time.
+
+---
+
+### ROC Curve
+
+
+| Binary Classification ROC Curve | (OvR) ROC Curve – Multi-Class Classification |
+|-------|-------|
+| <img src="https://files.catbox.moe/iou784.png" width="490"/> | <img src="https://files.catbox.moe/pz3mv7.png" width="540"/> |
+
+
+
+**Observation:**
+The model achieves an AUC score close to **1.0**, which indicates excellent classification ability.
+
+---
+
+## Key Takeaways
+
+* Transfer Learning significantly reduces training time.
+* Data augmentation improves model robustness.
+* Proper evaluation metrics give deeper insight into model performance.
+
+---