继续训练篇：
（笔者这里是ssd_mobilenet_v2_quantized_300x300_coco.config）
修改 fine_tune_checkpoint: '/models-master/logs/turnout/model.ckpt-324833'
然后执行训练语句继续训练
Windows篇
1. Anaconda
2.下载Tensorflow model、 SSD预训练模型和文件库
链接1（Tensorflow model）https://github.com/tensorflow/models/tree/r1.13.0
解压到
C:\tensorflow1
并重命名为models
链接2（ssd） http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz
下载解压到
C:\tensorflow1\models\research\object_detection
链接3（文件库）
https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10 下载解压到
C:\tensorflow1\models\research\object_detection
链接4（protuf）https://github.com/google/protobuf/releases
安装protuf (非常容易出错，我这里选择的是win版 3.4) 将bin文件夹中的【protoc.exe】放到C:\Windows 并cmd进入models\research\目录下
protoc object_detection/protos/*.proto --python_out=.
3. 设置conda虚拟环境
C:\> conda create -n tensorflow1 pip python=3.6.7
C:\> activate tensorflow1
(tensorflow1) C:\> conda install tensorflow-gpu=1.13（使用官方model则tensorflow-gpu=1.15.0）
(tensorflow1) C:\> conda install -c anaconda protobuf
(tensorflow1) C:\> pip install pillow
(tensorflow1) C:\> pip install lxml
(tensorflow1) C:\> pip install Cython
(tensorflow1) C:\> pip install contextlib2
(tensorflow1) C:\> pip install jupyter
(tensorflow1) C:\> pip install matplotlib
(tensorflow1) C:\> pip install pandas
(tensorflow1) C:\> pip install opencv-python设置python环境变量
(tensorflow1) C:\> set PYTHONPATH=C:\tensorflow1\models;C:\tensorflow1\models\research;C:\tensorflow1\models\research\slim
4. 编译Protobuf
(tensorflow1) C:\> cd C:\tensorflow1\models\research
(tensorflow1) C:\tensorflow1\models\research>protoc object_detection/protos/*.proto --python_out=.
如果是tf2，python setup.py install有好多坑。。。。
然后找到setup.py文件（在C:\tensorflow1\models\research\object_detection\packages\tf1里面的setup.py文件install下载的东西我们上面已经下载过了，复制到research再执行一遍也行，主要是C:\tensorflow1\models\research\slim里面的setup.py），复制到C:\tensorflow1\models\research
执行:
(tensorflow1) C:\tensorflow1\models\research> python setup.py build
(tensorflow1) C:\tensorflow1\models\research> python setup.py install5. 标注图片
将训练图片和测试图片分别放在
C:\tensorflow1\models\research\object_detection\images\
中的train和test文件夹下
标注步骤请参阅 我之前写的训练yolov5模型的教程
6. 生成训练数据
运行
(tensorflow1) C:\tensorflow1\models\research\object_detection> python xml_to_csv.py
会在 \object_detection\images中生成两个文件
train_labels.csv 和 test_labels.csv
修改object_detection\目录下的generate_tfrecord.py
要修改的部分是
# TO-DO replace this with label map
def class_text_to_int(row_label):
if row_label == 'nine':
return 1
elif row_label == 'ten':
return 2
elif row_label == 'jack':
return 3
elif row_label == 'queen':
return 4
elif row_label == 'king':
return 5
elif row_label == 'ace':
return 6
else:
None
将这部分内容中的row_label==’ ’ 修改为要训练的类别，请根据自己的情况适当增删
比如
# TO-DO replace this with label map
def class_text_to_int(row_label):
if row_label == 'basketball':
return 1
elif row_label == 'shirt':
return 2
elif row_label == 'shoe':
return 3
else:
None然后生成TFRecord 文件
python generate_tfrecord.py --csv_input=images\train_labels.csv --image_dir=images\train --output_path=train.record
python generate_tfrecord.py --csv_input=images\test_labels.csv --image_dir=images\test --output_path=test.record
会在object_detection\目录下生成train.record和test.record文件
7. 创建Label Map
label map 文件为object_detection\training\labelmap.pbtxt
修改其内容，原内容是
item {
id: 1
name: 'nine'
}item {
id: 2
name: 'ten'
}item {
id: 3
name: 'jack'
}item {
id: 4
name: 'queen'
}item {
id: 5
name: 'king'
}item {
id: 6
name: 'ace'
}修改为自己的训练类别和对应ID，ID就是第7步中每个类别返回的数字
比如
if row_label == 'basketball':
return 1
则在labelmap.pbtxt中修改为
item {
id: 1
name: 'basketball'
}8. 配置训练文件
将C:\tensorflow1\models\research\object_detection\samples\configs中的ssd_mobilenet_v2_quantized_300x300_coco.config复制到object_detection\training文件夹中
然后修改该文件
1. num_classes : 修改为自己的训练类别
2. fine_tune_checkpoint : "C:/tensorflow1/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
## in train_input_reader section
3. input_path : "C:/tensorflow1/models/research/object_detection/train.record"
4. label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
5. num_examples：修改为\images\test 中的图片数量
## in eval_input_reader section
6. input_path : "C:/tensorflow1/models/research/object_detection/test.record"
7. label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
8.batch_size 调整为1-6，太大带不起 9. 开始训练
重新训练时需要清除training\中除了labelmap和ssd_mobilenet。。。以外的所有文件，否则会报错
train.py 原本位于 Object_detection下的legacy文件夹内，请移动到Object_detection下，然后运行
(tensorflow1) C:\tensorflow1\models\research\object_detection> python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v2_quantized_300x300_coco.config
当loss降到2以下的时候，就可以停止训练 ， 直接 Ctrl + C即可
继续训练模型：修改ssd_mobilenet_v2_quantized_300x300_coco.config文件
fine_tune_checkpoint : "C:/tensorflow1/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
改为
fine_tune_checkpoint: 'C:/tensorflow1/models/research/object_detection/training/model.ckpt-9827'
10. 生成.pb文件
python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/ssd_mobilenet_v2_quantized_300x300_coco.config --trained_checkpoint_prefix training/model.ckpt-XXXX --output_directory inference_graph
将model.ckpt-XXXX替换为training\目录中数值最大的一个文件，这步之后会在\object_detection\inference_graph中生成一个frozen_inference_graph.pb文件
11. 测试Tensorflow模型
修改Object_detection_image.py，将NUM_CLASSES的数值修改为训练的类别数量
将IMAGE_NAME修改为测试图片的路径
然后运行
(tensorflow1) C:\tensorflow1\models\research\object_detection> python Object_detection_image.py应该可以看到检测结果
12. 导出 frozen inference graph
(tensorflow1) C:\tensorflow1\models\research\object_detection> mkdir TFLite_model
(tensorflow1) C:\tensorflow1\models\research\object_detection> set CONFIG_FILE=C:\\tensorflow1\models\research\object_detection\training\ssd_mobilenet_v2_quantized_300x300_coco.config
(tensorflow1) C:\tensorflow1\models\research\object_detection> set CHECKPOINT_PATH=C:\\tensorflow1\models\research\object_detection\training\model.ckpt-XXXX
(tensorflow1) C:\tensorflow1\models\research\object_detection> set OUTPUT_DIR=C:\\tensorflow1\models\research\object_detection\TFLite_model注意将上述步骤中的model.ckpt-XXXX替换为training\目录中数值最大的一个文件
(tensorflow1) C:\tensorflow1\models\research\object_detection> python export_tflite_ssd_graph.py --pipeline_config_path=%CONFIG_FILE% --trained_checkpoint_prefix=%CHECKPOINT_PATH% --output_directory=%OUTPUT_DIR% --add_postprocessing_op=true之后会在 \object_detection\TFLite_model中生成tflite_graph.pb 和 tflite_graph.pbtxt
二、模型转换.pb 到 .tflite
执行
pb_to_tflite.py（自创）
如下
’‘’‘’‘’‘’‘’‘’‘’
import tensorflow as tf
in_path = "tflite_graph.pb" #只需换成你模型中的值就OK
# 模型输入节点
input_tensor_name = ["normalized_input_image_tensor"]
input_tensor_shape = {"normalized_input_image_tensor":[1,300,300,3]}
# 模型输出节点
classes_tensor_name = ['TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3']
converter = tf.lite.TFLiteConverter.from_frozen_graph(in_path,
input_tensor_name, classes_tensor_name,
input_tensor_shape)
converter.allow_custom_ops=True
converter.post_training_quantize = True
tflite_model = converter.convert()
open("SSD.tflite", "wb").write(tflite_model)
‘’‘’‘’‘’‘’‘’‘’‘ 在/media/liuxuwei/Windows/tensorflow1/models/research/object_detection/TFLite_model下新建txt文件
命名为labelmap.txt，这是TFlite所需要的label map文件。根据windows篇第6步中的训练类别，填写labelmap.txt
比如
basketball
shirt
shoe
到这里Tflite模型就完成了
