add guideline about object detection

+# How to make your own object detector 
+
+> ### with TF (TensorFlow) Object Detection API & GCP (Google Gloud Platform)
+
+EastFull: LEE DONGCHAN (huromech@gmail.com)
+
+
+
+## TF Object Detection API and GCP
+
+![1_1](./imgs/1_1.PNG)
+
+ 
+
+ Object detection is the task of recognizing what class an objects is and where it is located. So, this may be more complex and difficult to implement than a simple classification model. [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) uses pre-trained object detection model to make it easier to implement object detection functions. In addition, the model can be trained in the cloud using [Google Cloud Platform](https://cloud.google.com/gcp/?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-skws-all-all-trial-b-dr-1008076&utm_content=text-ad-none-any-DEV_c-CRE_109860918967-ADGP_Hybrid+%7C+AW+SEM+%7C+SKWS+%7C+US+%7C+en+%7C+Multi+~+Cloud-KWID_43700009609890930-kwd-19383198255&utm_term=KW_%2Bcloud-ST_%2Bcloud&gclid=CjwKCAiA4Y7yBRB8EiwADV1haezYvLCzbz2c6lJvwoLzlRAcCk7YaVJemA3VasM2MR4axqgmG02MqRoCS48QAvD_BwE) without configuring the hardware environment for learning. Therefore, this article will talk about how to make your own object detector using TensorFlow Object Detection API and Google Cloud Platform :D
+
+
+
+## Tabel of Contents
+
+0. Setting up the Google Cloud Platform
+1. Installing TensorFlow and TensorFlow Object Detection API
+2. Making dataset 
+3. Using the pre-trained SSD for Transfer Learning
+4. Training and Evaluation Jobs on Google Cloud ML Engine
+
+
+
+## 0. Setting up the Google Cloud Platform
+
+ Before we begin, we will do the following to use Google Cloud ML engine and Storage.
+
+> 1. [Create a project](https://cloud.google.com/resource-manager/docs/creating-managing-projects)
+> 2. [Modify a project's billing settings](https://cloud.google.com/billing/docs/how-to/modify-project)
+> 3. [Install the Google Cloud SDK](https://cloud.google.com/sdk/install) to use Google Cloud Storage and ML Engine in your workplace (*In my case, I installed Ubuntu 18.04 LTS terminal in Windows 10. For the installaion of terminal, see [Terminal tutorial at fast.ai](https://course.fast.ai/terminal_tutorial.html) and the installation of Goolge Cloud CLI, see [here](https://course.fast.ai/start_gcp.html#step-2-install-google-cli) at course of fast.ai*)
+> 4. [Enable the ML Engine APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component&_ga=2.43515109.-1978295503.1509743045)
+> 5. [Set up a Google Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) (*Or, you can use the following command in the Google Cloud CLI*:`gsutil mb gs://YOUR_UNIQUE_BUCKET_NAME`)
+
+
+
+ Next, you need to add a TPU-specific service account to give the cloud TPU access to the project. Enter the following command to get the name of the account:
+
+```
+curl -H "Authorization: Bearer $(gcloud auth print-access-token) \
+	https://ml.googleapis.com/v1/projects/YOUR_PROJECT_ID:getConfig
+```
+
+
+
+After command,  copy the `tpuServiceAccount`(it looks like `service-account-12345@cloud-tpu.iam.gserviceaccount.com`) and save then as environment variable:
+
+```
+export TPU_ACCOUNT=your-service-account
+```
+
+
+
+And, give the role of ML serviceAgent to the TPU service account:
+
+```
+gcloud projects add-iam-policy-binding YOUR_PROJECT_ID  \
+	--member serviceAccount:$TPU_ACCOUNT --role roles/ml.serviceAgent
+```
+
+
+
+## 1. Installing TensorFlow and TensorFlow Object Detection API
+
+ You can install the TensorFlow Object Detection API by following [this installaion](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). Please refer to [here](https://www.tensorflow.org/install/) for more detailed procedures on TensorFlow. (*Notes: In my case, the version under tensorflow 1.13 and tensorflow 2.x did not work properly. I used the tensorflow 1.14.0 in a number of attempts :D - as of Feb, 2020*)
+
+
+
+## 2. Making dataset
+
+### 2.1. gathering data
+
+![1_2](./imgs/1_2.jpg)
+
+ There is still one more important process left! We need data before we build our detection model. In my case, I have collected several data to find a badprint that frequently occurs in 3D printing. Collect data for your object detector :D
+
+
+
+ ### 2.2. labeling data
+
+![1_3](./imgs/1_3.PNG)
+
+ Now, we label the data that we have collected. I used [VoTT (Visual Object Tagging Tool)](https://github.com/microsoft/VoTT) to carry out labelling and saved it in the form of a `.csv` file.
+
+In fact, it doesn't matter which labelling tool you use. However, if yo want to follow this article to configure the dataset, the information (about  `filename`, `xmin`, `ymin`, `xmax`, `ymax`, `class`) must be specified in the `.csv` file.
+
+
+
+### 2.3. generating TFRecords
+
+  In order to train the object detector, we have to create [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord). To do this, we will use `generate_tfrecord.py` from [datitran's raccon_dataset](https://github.com/datitran/raccoon_dataset). Before make TFRecords we need to change a few lines in the  `generate_tfrecord.py`.
+
+From:
+
+```python
+# TO-DO replace this with label map
+def class_text_to_int(row_label):
+    if row_label == 'raccoon':
+        return 1
+    else:
+        None
+```
+
+
+
+To:
+
+```python
+# TO-DO replace this with label map
+def class_text_to_int(row_label):
+    if row_label == 'bad':
+        return 1
+    elif row_label == 'WHAT YOU WANT !':
+        return 2
+    #.
+    #.
+    #.
+    else:
+        None
+```
+
+If you are using a different dataset, you must replace the class name. 
+
+
+
+Now our TFRecords can be generated by:
+
+```
+python generate_tfrecord.py \
+	--csv_input=YOUR_PATH/TRAIN_CSV_NAME.csv \
+	--output_path=YOUR_PATH/train.record \
+	--image_dir=YOUR_PATH/IMAGE_DIR
+	
+python generate_tfrecord.py \
+	--csv_input=YOUR_PATH/VALID_CSV_NAME.csv \
+	--output_path=YOUR_PATH/val.record \
+	--image_dir=YOUR_PATH/IMAGE_DIR
+```
+
+
+
+Next, we will place the `.tfrecord` on our Google Cloud Storage bucket.
+
+```
+gsutil -m cp -r YOUR_PATH/train.record gs://YOUR_UNIQUE_BUCKET_NAME/data/
+gsutil -m cp -r YOUR_PATH/val.record gs://YOUR_UNIQUE_BUCKET_NAME/data/
+```
+
+
+
+### 2.4. creating a label map
+
+ The label map maps an id to a name. This file is in `.pbtxt` format. In my case, I made it using the vim editor in terminal.
+
+```
+item {
+	id: 1
+	name: 'bad'
+}
+
+item {
+	id: 2
+	name: 'WHAT YOU WANT !'
+}
+
+	.
+	.
+	.
+```
+
+
+
+After creating a label map, we will place the `.pbtxt` on our Google Cloud Storage bucket.
+
+```
+gsutil -m cp -r YOUR_PATH/LABEL_MAP.pbtxt gs://YOUR_UNIQUE_BUCKET_NAME/data/
+```
+
+
+
+## 3. Using the pre-trained SSD for Transfer Learning
+
+### 3.1. downloading model
+
+ There are many pre-trained models we can use. We will use the checkpoints from these pre-trained models and apply to our task. What makes this possible is Transfer Learning. This is a strategy that enables pretty good performance with less time and data. Anyway, please check out the [Object Detection Models Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
+
+```
+wget http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz
+
+tar xzf ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz
+```
+
+So here we're going to use the ssd model based on the mobilenet. If you want a different model, try it! After downloading the model, upload the checkpoints of the model to Google Cloud Storage:
+
+```
+gsutil cp ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/model.ckpt.* gs://YOUR_UNIQUE_BUCKET_NAME/data/
+```
+
+
+
+### 3.2. configuring the object detection pipeline
+
+ In the TensorFlow Object Detection API, the model parameters, training and eval parameters are defined by a `.config` file. More details in [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md). Anyway, we'll need to configure some paths in order for the template to work. It will be  `PATH_TO_BE_CONFIGURED` in the code. Copy and move the config file corresponding to the model received for modification.
+
+```
+cp YOUR_PATH/models/research/object_detection/samples/configs/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config .
+```
+
+
+
+First, modify the `PATH_TO_BE_CONFIGURED` of the copied file to our Google Cloud Storage bucket.
+
+```
+sed -i "s|PATH_TO_BE_CONFIGURED|"gs://YOUR_UNIQUE_BUCKET_NAME"/data|g" ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config
+```
+
+
+
+In addition, modify the other things (`input_path`, `output_path`,`batch_size`, `num_steps`, `total_steps`, and so on).
+
+From:
+
+```
+	.
+	.
+train_config: {
+  fine_tune_checkpoint: "gs://YOUR_UNIQUE_BUCKET_NAME/data/model.ckpt"
+  batch_size: 64 #
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 25000 #
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  	.
+  	.
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "gs://YOUR_UNIQUE_BUCKET_NAME/data/mscoco_train.record-00000-of-00100" #
+  }
+  label_map_path: "gs://YOUR_UNIQUE_BUCKET_NAME/data/mscoco_label_map.pbtxt" #
+}
+	.
+	.
+```
+
+
+
+To:
+
+```
+	.
+	.
+train_config: {
+  fine_tune_checkpoint: "gs://YOUR_UNIQUE_BUCKET_NAME/data/model.ckpt"
+  batch_size: 16 #
+  sync_replicas: true
+  startup_delay_steps: 0
+  replicas_to_aggregate: 8
+  num_steps: 2000 #
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  	.
+  	.
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "gs://YOUR_UNIQUE_BUCKET_NAME/data/train.record" #
+  }
+  label_map_path: "gs://YOUR_UNIQUE_BUCKET_NAME/data/LABEL_MAP..pbtxt" #
+}
+	.
+	.
+```
+
+This is just an example. Therefore, you should modify the `.config` as you intended.
+
+
+
+Just like any other files, upload the `.config` to your Google Cloud Storage bucket.
+
+```
+gsutil cp ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config gs://YOUR_UNIQUE_BUCKET_NAME/data/pipeline.config
+```
+
+We're ready now! Let's train.
+
+
+
+## 4. Training and Evaluation Jobs on Google Cloud ML Engine
+
+ Before we start our training job in Google Cloud ML Engine, we need to package  the API, pycocotools and TF slim. 
+
+```
+# From YOUR_PATH/models/research/
+bash object_detection/dataset_tools/create_pycocotools_package.sh /tmp/pycocotools
+python setup.py sdist
+(cd slim && python setup.py sdist)
+```
+
+
+
+And start training! Run following gcloud command:
+
+```
+gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%s` \
+ --job-dir=gs://YOUR_UNIQUE_BUCKET_NAME/train \
+ --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+ --module-name object_detection.model_tpu_main \
+ --runtime-version 1.14 \
+ --scale-tier BASIC_TPU \
+ --region us-central1 \
+ -- \
+ --model_dir=gs://YOUR_UNIQUE_BUCKET_NAME/train \
+ --tpu_zone us-central1 \
+ --pipeline_config_path=gs://YOUR_UNIQUE_BUCKET_NAME/data/pipeline.config
+```
+
+
+
+After we start our training , run following command:
+
+```
+gcloud ml-engine jobs submit training `whoami`_object_detection_eval_validation_`date +%s` \
+ --job-dir=gs://YOUR_UNIQUE_BUCKET_NAME/train \
+ --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
+ --module-name object_detection.model_main \
+ --runtime-version 1.14 \
+ --scale-tier BASIC_GPU \
+ --region us-central1 \
+ -- \
+ --model_dir=gs://YOUR_UNIQUE_BUCKET_NAME/train \
+ --pipeline_config_path=gs://YOUR_UNIQUE_BUCKET_NAME/data/pipeline.config \
+ --checkpoint_dir=gs://YOUR_UNIQUE_BUCKET_NAME/train
+```
+
+
+
+That's all! You can use TensorBoard to see the accuracy of  your model.
+
+```
+# This command needs to be run once to allow your local machine to access your
+# GCS bucket.
+gcloud auth application-default login
+
+tensorboard --logdir=gs://YOUR_UNIQUE_BUCKET_NAME/model_dir
+```
+
+![1_4](./imgs/1_4.PNG)
+
+
+