我有标注好的xml格式数据集，帮我写个可以训练识别出人，球，鞋子这三类物体的目标检测代码，使用 EfficientNet-Lite0 模型进行训练

好的，这里提供一个使用 TensorFlow Object Detection API 进行目标检测的示例代码，使用的模型是 EfficientDet-Lite0。在这之前需要先安装 TensorFlow Object Detection API 和 EfficientDet-Lite0 模型。

import tensorflow as tf
import numpy as np
import cv2
import os

# 定义类别
CLASSES = ['person', 'ball', 'shoe']

# 读取标注数据
def read_annotation(xml_path):
    with open(xml_path, 'r') as f:
        xml_str = f.read()
    xml = tf.compat.v1.compat.v1.string_to_xml(xml_str)
    boxes = []
    labels = []
    for obj in xml.findall('object'):
        label = obj.find('name').text
        if label not in CLASSES:
            continue
        xmin = int(obj.find('bndbox/xmin').text)
        ymin = int(obj.find('bndbox/ymin').text)
        xmax = int(obj.find('bndbox/xmax').text)
        ymax = int(obj.find('bndbox/ymax').text)
        boxes.append([xmin, ymin, xmax, ymax])
        labels.append(CLASSES.index(label))
    return np.array(boxes), np.array(labels)

# 定义数据集
class DetectionDataset(tf.keras.utils.Sequence):
    def __init__(self, xml_dir, batch_size):
        self.xml_files = [os.path.join(xml_dir, f) for f in os.listdir(xml_dir) if f.endswith('.xml')]
        self.batch_size = batch_size

    def __len__(self):
        return len(self.xml_files) // self.batch_size

    def __getitem__(self, idx):
        batch_xml = self.xml_files[idx*self.batch_size:(idx+1)*self.batch_size]
        batch_images = []
        batch_boxes = []
        batch_labels = []
        for xml_path in batch_xml:
            image_path = xml_path.replace('.xml', '.jpg')
            image = cv2.imread(image_path)
            boxes, labels = read_annotation(xml_path)
            batch_images.append(image)
            batch_boxes.append(boxes)
            batch_labels.append(labels)
        inputs = {
            'image': np.array(batch_images)
        }
        outputs = {
            'bbox': np.array(batch_boxes),
            'label': np.array(batch_labels)
        }
        return inputs, outputs

# 定义模型
def build_model():
    image_input = tf.keras.layers.Input(shape=(None, None, 3), name='image')
    backbone = tf.keras.applications.EfficientNetLite0(include_top=False, weights='imagenet')
    x = backbone(image_input)
    x = tf.keras.layers.Conv2D(1280, kernel_size=1, padding='same', activation='relu')(x)
    x = tf.keras.layers.Conv2D(256, kernel_size=1, padding='same', activation='relu')(x)
    class_output = tf.keras.layers.Conv2D(len(CLASSES), kernel_size=1, padding='same', activation='softmax', name='class_output')(x)
    bbox_output = tf.keras.layers.Conv2D(4, kernel_size=1, padding='same', name='bbox_output')(x)
    model = tf.keras.models.Model(inputs=image_input, outputs=[class_output, bbox_output])
    return model

# 训练模型
def train_model(xml_dir, epochs, steps_per_epoch, batch_size):
    dataset = DetectionDataset(xml_dir, batch_size)
    model = build_model()
    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
    model.compile(optimizer=optimizer, loss=['sparse_categorical_crossentropy', 'mse'])
    model.fit(dataset, epochs=epochs, steps_per_epoch=steps_per_epoch)

# 测试模型
def test_model(image_path, model_path):
    model = tf.keras.models.load_model(model_path)
    image = cv2.imread(image_path)
    inputs = np.expand_dims(image, axis=0)
    outputs = model.predict(inputs)
    class_predictions = outputs[0][0]
    bbox_predictions = outputs[1][0]
    for i in range(len(CLASSES)):
        class_prediction = class_predictions[..., i]
        bbox_prediction = bbox_predictions[..., i]
        class_indices = np.where(class_prediction > 0.5)
        bboxes = bbox_prediction[class_indices]
        for bbox in bboxes:
            xmin, ymin, xmax, ymax = bbox
            cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
            cv2.putText(image, CLASSES[i], (int(xmin), int(ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    cv2.imshow('result', image)
    cv2.waitKey(0)

# 训练模型
train_model('path/to/xml/dir', epochs=10, steps_per_epoch=100, batch_size=8)

# 测试模型
test_model('path/to/image', 'path/to/model.h5')

其中，`read_annotation` 函数用于读取标注数据，`DetectionDataset` 类用于定义数据集，`build_model` 函数用于构建模型，`train_model` 函数用于训练模型，`test_model` 函数用于测试模型。在训练模型时，需要提供标注数据所在的目录，训练轮数，每轮训练步数和批次大小。在测试模型时，需要提供要测试的图片路径和训练好的模型路径。