這里面有很多指標(biāo)曲線(xiàn)，甚至有模型網(wǎng)絡(luò)架構(gòu)，筆者對(duì)于這里面很多指標(biāo)含義還沒(méi)有弄明白，不過(guò)感覺(jué)出TensorBoard這個(gè)工具應(yīng)該是極其強(qiáng)大。不過(guò)我們可以通過(guò)Total_Loss來(lái)看整體訓(xùn)練的情況。

從整體上看，loss曲線(xiàn)確實(shí)是收斂的，整體的訓(xùn)練效果還是滿(mǎn)意的。另外，TensorFlow還提供了訓(xùn)練過(guò)程中利用驗(yàn)證集驗(yàn)證準(zhǔn)確性的能力，但是筆者在調(diào)用時(shí)，仍有些問(wèn)題，這里暫時(shí)就不詳細(xì)說(shuō)明了。

Freeze Model模型導(dǎo)出：

查看模型實(shí)際的效果前，我們需要把訓(xùn)練的過(guò)程文件導(dǎo)出，生產(chǎn).pb的模型文件。本來(lái)，tensorflow/python/tools/freeze_graph.py提供了freeze model的api，但是需要提供輸出的final node names（一般是softmax之類(lèi)的最后一層的激活函數(shù)命名），而object detection api提供提供了預(yù)訓(xùn)練好的網(wǎng)絡(luò)，final node name并不好找，所以object_detection目錄下還提供了export_inference_graph.py。
python export_inference_graph.py
--input_type image_tensor
--pipeline_config_path D:/training-sets /data-translate/training/ssd_mobilenet_v1_pets.config
--trained_checkpoint_prefix D:/training-sets /data-translate/training/ssd_mobilenet_v1_pets.config /model.ckpt-*
--output_directory D:/training-sets /data-translate/training/result

導(dǎo)出完成后，在output_directory下，會(huì)生成frozen_inference_graph.pb、model.ckpt.data-00000-of-00001、model.ckpt.meta、model.ckpt.data文件。

調(diào)用生成模型：

目錄下本身有一個(gè)調(diào)用的例子，稍微改造如下：
import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

class TOD(object):
def __init__(self):
self.PATH_TO_CKPT = r'D:lib f-modelmodels-masterobject_detection rainingfrozen_inference_graph.pb'
self.PATH_TO_LABELS = r'D:lib f-modelmodels-masterobject_detection rainingsunglasses_label_map.pbtxt'
self.NUM_CLASSES = 1
self.detection_graph = self._load_model()
self.category_index = self._load_label_map()

def _load_model(self):
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def,)
return detection_graph

def _load_label_map(self):
label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map,
max_num_classes=self.NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
return category_index

def detect(self, image):
with self.detection_graph.as_default():
with tf.Session(graph=self.detection_graph) as sess:
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image, axis=0)
image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
self.category_index,
use_normalized_coordinates=True,
line_thickness=8)

cv2.namedWindow("detection", cv2.WINDOW_NORMAL)
cv2.imshow("detection", image)
cv2.waitKey(0)

if __name__ == '__main__':
image = cv2.imread('image.jpg')
detecotr = TOD()
detecotr.detect(image)

下面是一些圖片的識(shí)別效果：