使用tflite进行自定义对象检测

import tensorflow as tf

# 加载SavedModel格式的模型
model = tf.saved_model.load('path_to_saved_model')

# 转换为TFLite模型
converter = tf.lite.TFLiteConverter.from_saved_model('path_to_saved_model')
tflite_model = converter.convert()

# 保存TFLite模型
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

# 使用量化技术转换模型
converter = tf.lite.TFLiteConverter.from_saved_model('path_to_saved_model')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quant_model = converter.convert()

# 保存量化后的TFLite模型
with open('model_quant.tflite', 'wb') as f:
    f.pywrite(f)

# 使用混合精度量化转换模型
converter = tf.lite.TFLiteConverter.from_saved_model('path_to_saved_model')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_quant_model = converter.convert()

# 保存混合精度量化后的TFLite模型
with open('model_quant_mixed.tflite', 'wb') as f:
    f.write(tflite_quant_model)