使用Tensorflow训练自己的数据

训练自己的数据集(以bottle为例)：

 

1、准备数据

文件夹结构：
models
├── images
├── annotations
│ ├── xmls
│ └── trainval.txt
└── bottle
├── train_logs 训练文件夹
└── val_logs 日志文件夹

 

1)、下载官方预训练模型： https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md 
以ssd_mobilenet_v1_coco为例，将压缩包内model.ckpt*的三个文件复制到bottle内

2)、准备jpg图片数据，放入images文件夹（图片文件命名要求“名字+下划线+编号.jpg”,必须使用下划线，编号从1开始） 
使用https://github.com/tzutalin/labelImg工具对图片进行标注，生成xml文件放置xmls文件夹，并保持xml和jpg命名相同 
3)、新建 bottle/trainval.txt 文件，内容为（图片名 1 1 1），每行一个文件，如：

bottle_1 1 1 1
bottle_2 1 1 1

 

4)、新建object_detection/data/bottle_label_map.pbtxt,内容如下

item {
    id: 1
    name: 'bottle'
}

 

2、生成数据

# From tensorflow/models
python object_detection/create_pet_tf_record.py \
--label_map_path=object_detection/data/bottle_label_map.pbtxt \
--data_dir=`pwd` \
--output_dir=`pwd`

 

得到 pet_train.record 和 pet_val.record 移动至bottle文件夹

 

3、准备conf文件

复制object_detection/samples/configs/ssd_mobilenet_v1_pets.config到 /bottle/ssd_mobilenet_v1_bottle.config 
对ssd_mobilenet_v1_bottle.config文件进行一下修改：

修改第9行为 num_classes: 1，此数值代表bottle_label_map.pbtxt文件配置item的数量
修改第158行为 fine_tune_checkpoint: "bottle/model.ckpt"
修改第177行为 input_path: "bottle/pet_train.record"
修改第179行和193行为 label_map_path: "object_detection/data/bottle_label_map.pbtxt"
修改第191行为 input_path: "bottle/pet_val.record"

 

 

4、训练

# From tensorflow/models
python object_detection/train.py \
--logtostderr \
--pipeline_config_path=bottle/ssd_mobilenet_v1_bottle.config \
--train_dir=bottle/train_logs \
2>&1 | tee bottle/train_logs.txt &

 

 

5、验证

# From tensorflow/models
python object_detection/eval.py \
--logtostderr \
--pipeline_config_path=bottle/ssd_mobilenet_v1_bottle.config \
--checkpoint_dir=bottle/train_logs \
--eval_dir=bottle/val_logs &

 

 

6、可视化log

可一边训练一边可视化训练的log，可看到Loss趋势。

tensorboard --logdir train_logs/

 

浏览器访问 ip:6006，可看到趋势以及具体image的预测结果

 

7、导出模型

# From tensorflow/models
python object_detection/export_inference_graph.py \
--input_type image_tensor \
--pipeline_config_path bottle/ssd_mobilenet_v1_bottle.config \
--trained_checkpoint_prefix bottle/train_logs/model.ckpt-8 \
--output_directory bottle

 

生成 bottle/frozen_inference_graph.pb 文件

 

8、测试图片

运行object_detection_tutorial.ipynb并修改其中的各种路径即可 
或自写编译inference脚本，如tensorflow/models/object_detection/infer.py：

import sys
sys.path.append('..')
import os
import time
import tensorflow as tf
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
from utils import label_map_util
from utils import visualization_utils as vis_util
PATH_TEST_IMAGE = sys.argv[1]
PATH_TO_CKPT = 'VOC2012/frozen_inference_graph.pb'
PATH_TO_LABELS = 'VOC2012/pascal_label_map.pbtxt'
NUM_CLASSES = 21
IMAGE_SIZE = (18, 12)
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with detection_graph.as_default():
with tf.Session(graph=detection_graph, config=config) as sess:
start_time = time.time()
print(time.ctime())
image = Image.open(PATH_TEST_IMAGE)
image_np = np.array(image).astype(np.uint8)
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
print('{} elapsed time: {:.3f}s'.format(time.ctime(), time.time() - start_time))
vis_util.visualize_boxes_and_labels_on_image_array(
image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores),
category_index, use_normalized_coordinates=True, line_thickness=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)

 

运行infer.py test_images/image1.jpg即可