in my case I use google images to find [chinook] photos , I gather 200 sample

convert all images extensoins to xx.jpg , using Format Factory http://www.pcfreetime.com/

go to images directory in my case chinook and type in cmd python renameFiles.py

using labelImg https://github.com/tzutalin/labelImg unzip labelImg
run cmd and go to labelImg dir

conda install pyqt=5 
pyrcc5 -o resources.py resources.qrc
python labelImg.py

all my samples = 200 ,130 for train ,70 for test

pip install --upgrade tensorflow-gpu

conda create -n tensorflow1 pip python=3.5 
activate tensorflow1 
pip install --ignore-installed --upgrade tensorflow-gpu

other necessary packages

(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 jupyter 
(tensorflow1) C:\> pip install matplotlib 
(tensorflow1) C:\> pip install pandas 
(tensorflow1) C:\> pip install opencv-python 

https://github.com/tensorflow/models.git

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

https://github.com/seraj94ai/Train-Object-Detection-Classifier.git unzip folder and copy paste in C:\tensorflow1\models\research\object_detection open cmd

cd C:\tensorflow1\models\research\object_detection
mkdir images
mkdir inference_graph
mkdir training

Configure PYTHONPATH environment variable

PYTHONPATH variable must be created that points to the directories \models
\models\research
\models\research\slim

set PYTHONPATH=C:\tensorflow1\models;C:\tensorflow1\models\research;C:\tensorflow1\models\research\slim
echo %PYTHONPATH%
set PATH=%PATH%;PYTHONPATH
echo %PATH%

Protobuf (Protocol Buffers) libraries must be compiled , it used by TensorFlow to configure model and training parameters Open Anaconda Prompt and go to C:\tensorflow1\models\research

protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto

(tensorflow1) C:\tensorflow1\models\research> python setup.py build
(tensorflow1) C:\tensorflow1\models\research> python setup.py install

Test TensorFlow setup to verify it works (tensorflow1) C:\tensorflow1\models\research\object_detection> jupyter notebook object_detection_tutorial.ipynb

TFRecords is an input data to the TensorFlow training model creat .csv files from .xml files

cd C:\tensorflow1\models\research\object_detection
python xml_to_csv.py

This creates a train_labels.csv and test_labels.csv file in the \object_detection\images folder.

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

edit edit generate_tfrecord.py and put your classes names

go to \data copy pet_label_map.pbtxt to \training dir and rename it to labelmap.pbtxt

edit it to your class chinook

cd C:\tensorflow1\models\research\object_detection\samples\configs copy faster_rcnn_inception_v2_pets.config past it in \training dir and edit it

In the model section change num_classes to number of different classes

fine_tune_checkpoint : C:/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt

In the train_input_reader section change input_path and label_map_path as :
Input_path : C:/tensorflow1/models/research/object_detection/train.record
Label_map_path: C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt

In the eval_config section change num_examples as :
Num_examples = number of files in \images\test directory.

In the eval_input_reader section change input_path and label_map_path as :
Input_path : C:/tensorflow1/models/research/object_detection/test.record
Label_map_path: C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt

python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

in cmd type (tensorflow1) C:\tensorflow1\models\research\object_detection>tensorboard --logdir=training

training is complete ,the last step is to generate the frozen inference graph (.pb file) change “XXXX” in “model.ckpt-XXXX” should be replaced with the highest-numbered .ckpt file in the training folder:

python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/faster_rcnn_inception_v2_pets.config --trained_checkpoint_prefix training/model.ckpt-XXXX --output_directory inference_graph

You can use “echo %PATH%” and “echo %PYTHONPATH%” to check the environment variables and make sure they are set up correctly. Also, make sure you have run these commands from the \models\research directory:

setup.py build
setup.py install

ImportError: cannot import name 'string_int_label_map_pb2' (or similar errors with other pb2 files) This occurs when the protobuf files (in this case, preprocessor.proto) have not been compiled go to step 12

in C:\tensorflow1\models\research\object_detection\utils\learning_schedules.py

replace range(num_boundaries) to [i for i in range(num_boundaries)]

(tensorflow1) C:\tensorflow1\models\research> python setup.py build
(tensorflow1) C:\tensorflow1\models\research> python setup.py install

in C:\tensorflow1\models\research\object_detection\generate_tfrecord.py replace [else: None] by [else: return 0]