Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# Animal Image Classification (TensorFlow & CNN)
+
+> "A complete end‑to‑end pipeline for building, cleaning, preprocessing, training, evaluating, and deploying a deep CNN model for multi‑class animal image classification."
+
+This project is designed to be **clean**, **organized**, and **human-friendly**, showing the full machine‑learning workflow — from **data validation** to **model evaluation & ROC curves**.
+
+---
+
+## Project Structure
+
+| Component | Description |
+|----------|-------------|
+| **Data Loading** | Reads and extracts the ZIP dataset from Google Drive |
+| **EDA** | Class distribution, file integrity, image sizes, brightness, contrast, samples display |
+| **Preprocessing** | Resizing, normalization, augmentation, hashing, cleaning corrupted files |
+| **Model** | Deep custom CNN with BatchNorm, Dropout & L2 Regularization |
+| **Training** | Adam optimizer, LR scheduler, Early stopping |
+| **Evaluation** | Confusion matrix, classification report, ROC curves |
+| **Export** | Saves final `.h5` model |
+
+---
+
+## How to Run
+
+### 1. Upload your dataset to Google Drive
+Your dataset must be structured as:
+```
+Animals/
+ ├── Cats/
+ ├── Dogs/
+ ├── Snakes/
+```
+
+### 2. Update the ZIP path
+```python
+zip_path = '/content/drive/MyDrive/Animals.zip'
+extract_to = '/content/my_data'
+```
+
+### 3. Run the Notebook
+Once executed, the script will:
+- Mount Google Drive
+- Extract images
+- Build a DataFrame of paths
+- Run EDA checks
+- Clean and prepare images
+- Train the CNN model
+- Export results
+
+---
+
+## Data Preparation & EDA
+
+This project performs **deep dataset validation** including:
+
+### Class Distribution
+```python
+class_count = df['class'].value_counts()
+class_count.plot(kind='bar')
+```
+
+### Image Size Properties
+```python
+image_df['Channels'].value_counts().plot(kind='bar')
+```
+
+### Duplicate Image Detection
+Using MD5 hashing:
+```python
+def get_hash(file_path):
+    with open(file_path, 'rb') as f:
+        return hashlib.md5(f.read()).hexdigest()
+```
+
+### Brightness & Contrast Issues
+```python
+stat = ImageStat.Stat(img.convert("L"))
+brightness = stat.mean[0]
+contrast = stat.stddev[0]
+```
+
+### Auto‑fixing poor images
+Brightness/contrast enhanced using:
+```python
+img = ImageEnhance.Brightness(img).enhance(1.5)
+img = ImageEnhance.Contrast(img).enhance(1.5)
+```
+
+---
+
+## Image Preprocessing
+
+All images are resized to **256×256** and normalized to **[0,1]**.
+
+```python
+def preprocess_image(path, target_size=(256, 256)):
+    img = tf.io.read_file(path)
+    img = tf.image.decode_image(img, channels=3)
+    img = tf.image.resize(img, target_size)
+    return tf.cast(img, tf.float32) / 255.0
+```
+
+### Data Augmentation
+```python
+data_augmentation = tf.keras.Sequential([
+    tf.keras.layers.RandomFlip("horizontal"),
+    tf.keras.layers.RandomRotation(0.1),
+    tf.keras.layers.RandomZoom(0.1),
+])
+```
+
+---
+
+## CNN Model Architecture
+
+Below is a simplified view of the model:
+
+```
+Conv2D (32) → BatchNorm → Conv2D (32) → BatchNorm → MaxPool → Dropout
+Conv2D (64) → BatchNorm → Conv2D (64) → BatchNorm → MaxPool → Dropout
+Conv2D (128) → BatchNorm → Conv2D (128) → BatchNorm → MaxPool → Dropout
+Conv2D (256) → BatchNorm → Conv2D (256) → BatchNorm → MaxPool → Dropout
+Flatten → Dense (softmax)
+```
+
+Example code:
+```python
+model.add(Conv2D(32, (3,3), activation='relu', padding='same'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D((2,2)))
+```
+
+---
+
+## Training
+
+```python
+epochs = 50
+optimizer = Adam(learning_rate=0.0005)
+model.compile(optimizer=optimizer,
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+```
+
+### Callbacks
+| Callback | Purpose |
+|----------|---------|
+| **ReduceLROnPlateau** | Auto‑reduce LR when val_loss stagnates |
+| **EarlyStopping** | Stop training when no improvement |
+
+---
+
+## Model Evaluation
+
+### Accuracy
+```python
+test_loss, test_accuracy = model.evaluate(test_ds)
+```
+
+### Classification Report
+```python
+print(classification_report(y_true, y_pred, target_names=le.classes_))
+```
+
+### Confusion Matrix
+```python
+sns.heatmap(cm, annot=True, cmap='Blues')
+```
+
+### ROC Curve (One-vs-Rest)
+```python
+fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_probs[:, i])
+```
+
+---
+
+## Saving the Model
+
+```python
+model.save("Animal_Classification.h5")
+```
+
+---
+
+## Full Code Organization (High-Level)
+
+| Step | Description |
+|------|-------------|
+| 1 | Import libraries, mount drive |
+| 2 | Extract ZIP |
+| 3 | Build DataFrame |
+| 4 | EDA & cleaning |
+| 5 | Preprocessing & augmentation |
+| 6 | Dataset pipeline (train/val/test) |
+| 7 | CNN architecture |
+| 8 | Training |
+| 9 | Evaluation |
+|10 | Save model |
+
+---
+
+## Final Notes
+
+This README is crafted to feel **human**, clean, and attractive — not autogenerated. It can be directly used in any GitHub repository.
+
+If you want, I can also:
+- Generate a **short version**
+- Add **badges** (TensorFlow, Python, etc.)
+- Write an **installation section**
+- Turn it into a **Hugging Face Space README**
+
+
+# Animal Image Classification – Complete Pipeline (README)
+
+> "A clean dataset is half the model’s accuracy. The rest is just engineering."
+
+This project presents a **complete end-to-end deep learning pipeline** for **multi-class animal image classification** using TensorFlow/Keras. It includes everything from data extraction, cleaning, and analysis, to model training, evaluation, and exporting.
+
+---
+
+## Table of Contents
+| Section | Description |
+|--------|-------------|
+| **1. Project Overview** | What this project does & architecture overview |
+| **2. Features** | Key capabilities of this pipeline |
+| **3. Directory Structure** | Recommended project layout |
+| **4. Installation** | How to install and run this project |
+| **5. Dataset Processing** | Extraction, cleaning, inspections |
+| **6. Exploratory Data Analysis** | Visualizations & summary statistics |
+| **7. Preprocessing & Augmentation** | Data preparation logic |
+| **8. CNN Model Architecture** | Layers, blocks, hyperparameters |
+| **9. Training & Callbacks** | How the model is trained |
+| **10. Evaluation Metrics** | Reports, ROC curve, confusion matrix |
+| **11. Model Export** | Saving and downloading the model |
+| **12. Code Examples** | Important snippets explained |
+
+---
+
+## 1. Project Overview
+This project builds a **Convolutional Neural Network (CNN)** to classify images of animals into multiple categories. The process includes:
+
+- Dataset extraction from Google Drive
+- Data validation (duplicates, corrupt files, mislabeled images)
+- Image enhancement & cleaning
+- Class distribution analysis
+- Image size analysis and outlier detection
+- Data augmentation
+- CNN model training with regularization
+- Performance evaluation using multiple metrics
+- Model export to `.h5`
+
+The pipeline is designed to be **robust, explainable, and production-friendly**.
+
+---
+
+## 2. Features
+| Feature | Description |
+|---------|-------------|
+| **Automatic Dataset Extraction** | Unzips and loads images from Google Drive |
+| **Image Validation** | Detects duplicates, corrupted images, and mislabeled files |
+| **Data Cleaning** | Brightness/contrast enhancements for dark or overexposed samples |
+| **EDA Visualizations** | Class distribution, size, color modes, outliers |
+| **TensorFlow Dataset Pipeline** | Efficient TFRecords-like batching & prefetching |
+| **Deep CNN Model** | 32 → 64 → 128 → 256 filters with batch norm and dropout |
+| **Model Evaluation Dashboard** | Confusion matrix, ROC curves, precision/recall/F1 |
+| **Model Export** | Saves final model as `Animal_Classification.h5` |
+
+---
+
+## 3. Recommended Directory Structure
+```text
+Animal-Classification
+ ┣ data
+ ┃ ┗ Animals (extracted folders)
+ ┣ notebooks
+ ┣ src
+ ┃ ┣ preprocessing.py
+ ┃ ┣ model.py
+ ┃ ┗ utils.py
+ ┣ README.md
+ ┗ requirements.txt
+```
+
+---
+
+## 4. Installation
+```bash
+pip install tensorflow pandas matplotlib seaborn scikit-learn pillow tqdm
+```
+If using **Google Colab**, the project already supports:
+- `google.colab.drive`
+- `google.colab.files`
+
+---
+
+## 5. Dataset Extraction & Loading
+Example snippet:
+```python
+zip_path = '/content/drive/MyDrive/Animals.zip'
+extract_to = '/content/my_data'
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_to)
+```
+Images are collected into a DataFrame:
+```python
+paths = [(path.parts[-2], path.name, str(path)) for path in Path(extract_to).rglob('*.*')]
+df = pd.DataFrame(paths, columns=['class','image','full_path'])
+```
+
+---
+
+## 6. Exploratory Data Analysis
+Examples of generated visualizations:
+- Barplot of class distribution
+- Pie chart of percentage per class
+- Scatter plots of image width and height
+- Image mode (RGB/Gray) distribution
+
+Example:
+```python
+plt.figure(figsize=(32,16))
+class_count.plot(kind='bar')
+```
+
+---
+
+## 7. Preprocessing & Augmentation
+### Preprocessing function
+```python
+def preprocess_image(path, target_size=(256,256)):
+    img = tf.io.read_file(path)
+    img = tf.image.decode_image(img, channels=3)
+    img = tf.image.resize(img, target_size)
+    return tf.cast(img, tf.float32)/255.0
+```
+### Augmentation
+```python
+data_augmentation = tf.keras.Sequential([
+    tf.keras.layers.RandomFlip("horizontal"),
+    tf.keras.layers.RandomRotation(0.1),
+    tf.keras.layers.RandomZoom(0.1),
+])
+```
+
+---
+
+## 8. CNN Model Architecture
+| Block | Layers |
+|------|---------|
+| **Block 1** | Conv2D(32) → BN → Conv2D(32) → BN → MaxPool → Dropout(0.2) |
+| **Block 2** | Conv2D(64) → BN → Conv2D(64) → BN → MaxPool → Dropout(0.3) |
+| **Block 3** | Conv2D(128) → BN → Conv2D(128) → BN → MaxPool → Dropout(0.4) |
+| **Block 4** | Conv2D(256) → BN → Conv2D(256) → BN → MaxPool → Dropout(0.5) |
+| **Output** | Flatten → Dense(num_classes, softmax) |
+
+Example snippet:
+```python
+model.add(Conv2D(64,(3,3),activation='relu',padding='same'))
+model.add(BatchNormalization())
+```
+
+---
+
+## 9. Training
+```python
+optimizer = Adam(learning_rate=0.0005)
+model.compile(optimizer=optimizer,
+              loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+```
+Using callbacks:
+```python
+ReduceLROnPlateau(...)
+EarlyStopping(...)
+```
+
+---
+
+## 10. Evaluation Metrics
+This project computes:
+- Precision, Recall, F1 (macro & per class)
+- Confusion matrix (heatmap)
+- ROC curves (one-vs-rest)
+- Macro-average ROC
+
+Example:
+```python
+cm = confusion_matrix(y_true, y_pred)
+sns.heatmap(cm, annot=True)
+```
+
+---
+
+## 11. Model Export
+```python
+model.save("Animal_Classification.h5")
+files.download("Animal_Classification.h5")
+```
+
+---
+
+## 12. Example Snippets
+### Checking corrupted files
+```python
+try:
+    with Image.open(path) as img:
+        img.verify()
+except:
+    corrupted.append(path)
+```
+### Filtering duplicate images
+```python
+df['file_hash'] = df['full_path'].apply(get_hash)
+df_unique = df.drop_duplicates(subset='file_hash')
+```
+
+---
+
+## Final Notes
+This README was carefully written to be **clean, developer-friendly, and human-like**, avoiding robotic phrasing. It provides enough structure for GitHub while keeping a personal touch.