Naomi

Setup

Clone models repostory

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

Compile Proto Buffer (protobof) inside models/research directory

protoc object_detection/protos/*.proto --python_out=.

And then export $PYTHONPATH variable inside models/research directory

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Setuptools

sudo python3 setup.py install

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Graph

Loss

Batch

Box Predictor (Historgram)

Steps

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Usage

Convert dataset meta labels xml to csv

python3 xml_to_csv.py

Convert csv dataset labels to TFRecord file format

python3 generate_tfrecord.py --type=train --csv_input=data/train_labels.csv  --output_path=data/train.record
python3 generate_tfrecord.py --type=test --csv_input=data/test_labels.csv  --output_path=data/test.record

Grab the COCO models

wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz

or you can click here

Start training the model

python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

Export graph and the model we have trained

python3 export_inference_graph.py \
    --input_type image_tensor \
    --pipeline_config_path training/ssd_mobilenet_v1_pets.config \
    --trained_checkpoint_prefix training/model.ckpt-1000 \
    --output_directory plate_model_exported

Start jupyter notebook inside the directory to see the results

jupyter notebook --notebook-dir=.