Part 3 of 3: Model Usage — Transfer learning using tensorflow’s object detection model on Mac

with GCloud Machine Learning Engine

Vivi E

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Published in

Coinmonks

3 min readJul 8, 2018

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This part is meant to be the continuation of Part 1: Data Preparation, and Part 2: Modelling.

Step 1. Download evaluation output, and training output from GCP Bucket.

# From the directory where you want to copy the files# create local folder for evaluation output
mkdir eval
# copy evaluation output to local folder
gsutil cp gs://vivienne-artifacts/object_detection_rat/eval/* eval/# create local folder for training output
mkdir train
# copy training output to local folder
gsutil cp gs://vivienne-artifacts/object_detection_rat/train/* train/

Training output is composed of .index, .data, and .meta files for each checkpoint. Make sure that the checkpoint trio are complete.

Step 2. Check the latest checkpoint

Download, and open the checkpoint file. The first line will reveal the latest checkpoint. This means that model.ckpt-20002 is the latest checkpoint

# Checkpoint file contains:model_checkpoint_path: "model.ckpt-20002"
all_model_checkpoint_paths: "model.ckpt-12316"
all_model_checkpoint_paths: "model.ckpt-14794"
all_model_checkpoint_paths: "model.ckpt-17271"
all_model_checkpoint_paths: "model.ckpt-19740"
all_model_checkpoint_paths: "model.ckpt-20002"

Step 3. Convert model checkpoints to a prototype model

Convert model checkpoints to Protobuf, in my case I chose the latest one.

# from /PATH/TO/tensorflow/models/researchpython object_detection/export_inference_graph.py \
    --input_type image_tensor \
    --pipeline_config_path /PATH/TO/ssd_mobilenet_v1_coco.config \
    --trained_checkpoint_prefix /PATH/TO/model.ckpt-20002 \
    --output_directory /PATH/TO/rat_object_detection/vivi_model

The code above produced a vivi_model directory that contains the following files:

checkpoint
frozen_inference_graph.pb
model.ckpt.data-00000-of-00001
model.ckpt.index
model.ckpt.meta
pipeline.config
saved_model/saved_model.pb
variables/

Step 4. Use model for object detection

I used the object detection tutorial to test if my model was exported correctly, and if it can detect rats. This tutorial assumes that you know how to use jupyter notebooks. Make sure that your jupyter notebook runs on the virtual environment for your object detection project.

# How to use your python virtual environment for your jupyter notebookworkon virtualenv_name
pip install ipykernel
ipython kernel install --user --name=virtualenv_name

Replace images in /PATH/TO/tensorflow/models/research/object_detection/test_images