Models: File "/home/hipstudents/anaconda3/envs/tensorflow_gpuenv/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/utils/object_detection_evaluation.py", line 203, in add_single_ground_truth_image_info raise ValueError('Image with id {} already added.'.format(image_id)) ValueError: Image with id b'xmap_10_12.png' already added.

[Jakaria08]

System information

What is the top-level directory of the model you are using: ~/
Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes, written scripts to convert .xml files to tf record 
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
TensorFlow installed from (source or binary): Compiled from source
TensorFlow version (use command below): 1.11.0
Bazel version (if compiling from source): 0.16.1
CUDA/cuDNN version: 9.0.176, cuDNN: 9.0
GPU model and memory: GeForce GTX1080Ti, 11GB
Exact command to reproduce: python eval.py --logtostderr --pipeline_config_path=training/ssd_mobilenet_v1_coco.config --checkpoint_dir=training/ --eval_dir=eval/

Image training is ok with ssd_mobilenet_v1_coco.
getting the error while testing:

File "/home/hipstudents/anaconda3/envs/tensorflow_gpuenv/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/utils/object_detection_evaluation.py", line 203, in add_single_ground_truth_image_info
raise ValueError('Image with id {} already added.'.format(image_id))

Please help.

generating tf record for test and train images.

`Usage:
# From tensorflow/models/
# Create train data:
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record
# Create test data:
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record

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

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 = flags.FLAGS

TO-DO replace this with label map

def class_text_to_int(row_label):
if row_label == 'Field':
return 1
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_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size

filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []

for index, row in group.object.iterrows():
    xmins.append(row['xmin'] / width)
    xmaxs.append(row['xmax'] / width)
    ymins.append(row['ymin'] / height)
    ymaxs.append(row['ymax'] / height)
    classes_text.append(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_jpg),
    '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(os.getcwd(), 'Images')
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()

[Jakaria08]

In the ssd_mobilenet_coco_v1.config file, num_examples was 8000. In my case, test dataset only has 121 samples. I forgot to update that and got new kind of error that I couldn't find on the Internet. As it is a silly mistake, so I think a very few people did that. this answer might help someone who will do this kind of mistake. I changed the following in the config file and the error is resolved:

eval_config: {
        #num of test images. In my case 121. Previously It was 8000
        num_examples: 121
        # Note: The below line limits the evaluation process to 10 evaluations.
        # Remove the below line to evaluate indefinitely.
        max_evals: 10
      }

[chowkamlee81]

My data is around 300. But code crashes with the same error once it evaluates more than 9 image evaluation. KIndly suggest on how to solve this problem

[natures66]

My data is around 300. But code crashes with the same error once it evaluates more than 9 image evaluation. KIndly suggest on how to solve this problem

hi do you solve your problem ? I am facing the same issue

[jhonjam]

hi, i am working in google clolab, the error is

File "/content/gdrive/My Drive/EntrenarSSD/models/research/object_detection/utils/object_detection_evaluation.py", line 316, in add_single_ground_truth_image_info
raise ValueError('Image with id {} already added.'.format(image_id))
ValueError: Image with id b'd057acd7541ee5b6.JPEG' already added.

i make the changes to:

eval_config: {
metrics_set: "coco_detection_metrics"
#metrics_set: "open_images_V2_detection_metrics"
num_examples: 8 # supusteamnete hay que cambiarlos por el numero de imagnes de tsts que tengamos
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}

but I still have the same problem

helpme please

[natures66]

I have solved this problem, just change the num_examples of eval_config. Because the number must be the real number of your image sets which real have objects. That means if you want to evaluate 500 images but actually there are only 300 images have objects, you should change 500 to 300.