To extend the extent of detection by Tensor Flow object detection APIs and Custom Detector built using apposite techniques and tools.
First, install the scripts requirements: pip install -r requirements.txt
Compile the Protobuf libraries: protoc object_detection/protos/*.proto --python_out=.
Add models and models/slim to your PYTHONPATH: export PYTHONPATH=$PYTHONPATH:pwd:pwd/slim
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Create the TensorFlow Records Run the script: python object_detection/create_tf_record.py
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Download a Base Model Models to download from this model zoo. Use faster_rcnn_resnet101_coco. Extract the files and move all the model.ckpt to our models directory.
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Train the Model Run the following script to train the model: python object_detection/train.py \ --logtostderr \ --train_dir=train \ --pipeline_config_path=faster_rcnn_resnet101.config
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Export the Inference Graph When you model is ready depends on your training data, the more data, the more steps you’ll need. My model was pretty solid at ~2.5k steps.
Move the model.ckpt files with the highest number to the root of the repo into a frozen inference graph by running this command: python object_detection/export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path faster_rcnn_resnet101.config \ --trained_checkpoint_prefix model.ckpt-STEP_NUMBER \ --output_directory output_inference_graph You should see a new output_inference_graph directory with a frozen_inference_graph.pb file. -
Test the Model python object_detection/object_detection_runner.py It will run your object detection model found at output_inference_graph/frozen_inference_graph.pb on all the images in the test_images directory and output the results in the output/test_images directory.