Update README.md

README.md

@@ -1,3 +1,78 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# Sports_Balls_Classification.h5
+
+## Model Details
+
+This is a trained InceptionV3 transfer learning model for classifying 15 different types of sports balls.
+
+## Specifications
+
+- Architecture: InceptionV3 with custom classification head
+- Input Size: 225 x 225 pixels (RGB)
+- Output Classes: 15 sports ball types
+- Framework: TensorFlow/Keras
+- Format: H5 (HDF5)
+
+## Supported Sports Ball Types
+
+1. American Football
+2. Baseball
+3. Basketball
+4. Billiard Ball
+5. Bowling Ball
+6. Cricket Ball
+7. Football
+8. Golf Ball
+9. Hockey Ball
+10. Hockey Puck
+11. Rugby Ball
+12. Shuttlecock
+13. Table Tennis Ball
+14. Tennis Ball
+15. Volleyball
+
+## Loading and Using
+
+### Python Example
+
+```python
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+model = tf.keras.models.load_model("Sports_Balls_Classification.h5")
+
+img = Image.open("sports_ball.jpg").convert("RGB")
+img = img.resize((225, 225))
+img_array = np.array(img).astype("float32") / 255.0
+img_array = np.expand_dims(img_array, axis=0)
+
+predictions = model.predict(img_array)
+predicted_class = np.argmax(predictions[0])
+confidence = np.max(predictions[0])
+```
+
+## Training Approach
+
+- Stage 1: Feature extraction (5 epochs) - Base frozen
+- Stage 2: Fine-tuning (10 epochs) - Last 30 layers unfrozen
+- Data balancing: 808 images per class
+- Callbacks: Early stopping, learning rate reduction, checkpointing
+
+## Performance
+
+Trained on balanced, preprocessed sports ball images with augmentation.
+Achieves high accuracy across all 15 sports ball classes.
+
+## Requirements
+
+- TensorFlow >= 2.0
+- Pillow
+- NumPy
+
+## License
+
+Part of the Sports Ball Classification project