Я обучил модель ssd_mobilenet_v1_coco из зоопарка на наборе данных с ~ 25000 фото дорожных знаков размером 48x48 пикселей: 
Процесс обучения выглядит хорошо (начался с ~ 15,5 и уменьшилсядо 0,0135): 
но когда я запускаю eval.py с тестовым набором данных, содержащим ~ 7k фото: 
и в конце я вижу ошибку: следующие классы имеютбезосновательных примеров истины [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 4546 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67]
Записи, сгенерированные из CSV с этимскрипт:
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import io
import pandas as pd
import tensorflow as tf
import sys
sys.path.append("C:\\Users\\Jekoc\\Desktop\\TRAINING\\rus\\models-master\\research\\")
sys.path.append("C:\\Users\\Jekoc\\Desktop\\TRAINING\\rus\\models-master\\research\\object_detection\\utils")
from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict
flags = tf.app.flags
flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
flags.DEFINE_string('image_dir', '', 'Path to images')
FLAGS = flags.FLAGS
# TO-DO replace this with label map
def class_text_to_int(row_label):
if row_label != 0:
return row_label
else:
None
def split(df, group):
data = namedtuple('data', ['filename', 'object'])
gb = df.groupby(group)
return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
def create_tf_example(group, path):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = Image.open(encoded_png_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'png'#changed from jpg to png
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(0 / width)
xmaxs.append(48 /width) # size is 48x48px so xmaxs=1
ymins.append(0 /height)
ymaxs.append(48 /height) # size is 48x48px so ymaxs=1
classes_text.append(str(row['class']).encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_png),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def main(_):
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
path = os.path.join(FLAGS.image_dir)
examples = pd.read_csv(FLAGS.csv_input)
grouped = split(examples, 'filename')
for group in grouped:
tf_example = create_tf_example(group, path)
writer.write(tf_example.SerializeToString())
writer.close()
output_path = os.path.join(os.getcwd(), FLAGS.output_path)
print('Successfully created the TFRecords: {}'.format(output_path))
if __name__ == '__main__':
tf.app.run()
Что я могу с этим сделать?заранее спасибо