| --- |
| 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 |
