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

MIT

AIOmarRehan/Sports_Balls_Classification_InceptionV3

Sports_Balls_Classification.h5

Model Details

Specifications

Supported Sports Ball Types

Loading and Using

Python Example

Training Approach

Performance

Requirements

License

AIOmarRehan/
Sports_Balls_Classification_InceptionV3