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To transform YoloV8 into Tflite format, follow these steps:

1. Install TensorFlow and Keras on your local machine.
2. Load the YoloV8 weights and architecture into Keras.
3. Convert the Keras model to a TensorFlow model using the TensorFlow converter:

import tensorflow as tf

# Load the Keras model
keras_model = load_model('yolov8.h5')

# Convert the model to TensorFlow format
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
tflite_model = converter.convert()

# Save the converted model
with open('yolov8.tflite', 'wb') as f:
    f.write(tflite_model)

1. Optimize the Tflite model using the TensorFlow Lite Optimizer:

import tensorflow as tf

# Load the Tflite model
tflite_model = open('yolov8.tflite', 'rb').read()

# Optimize the model
converter = tf.lite.TFLiteConverter.from_buffer(tflite_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the optimized model
with open('yolov8_optimized.tflite', 'wb') as f:
    f.write(tflite_model)

1. The YoloV8 model should now be in Tflite format and ready to use.