Please note that this repo uses TF1. TF2 for object detection is easier to install and get to work, so I would recommend using that instead. Documentation can be found here
This repository contains the data and code used for generating the result as present in our study. Here we provide instructions regarding how to start training.
Data and images are found in object_detection/images
https://github.com/tensorflow/models/tree/master/research/object_detection
Give python path direction variable to these folders
set PYTHONPATH=C:\river_plastic_detection;C:\river_plastic_detection\slim
set PATH=%PATH%;PYTHONPATH
cd C:/river_plastic_detection
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
python setup.py build
python setup.py install
Move to object_detection folder and create tf.record files
cd C:/river_plastic_detection/object_detection
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
Make sure dependencies and paths are correct
object_detection/samples/configs/faster_rcnn_inception_v2+coco.config
Change paths, number of classes and number of training examples in faster rcnn config file
python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_coco.config
After training, you have to export the inference graph with the following command. Make sure to replace the number with the highest step count.
python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/faster_rcnn_inception_v2_coco.config --trained_checkpoint_prefix training/model.ckpt-17196 --output_directory inference_graph
The frozen .pb should now be located in the inference_graph folder. There are some jupyter notebook scripts that can be used for testing.
Download https://github.com/pdollar/coco.git and run python setup.py install in coco/PythonAPI
python eval.py --logtosderr --checkpoint_dir=training/ --eval_dir=eval/ --pipeline_config_path=training/faster_rcnn_inception_v2_coco.config
Some error debugs that I used, not sure if necessary:
- Some error might occure that requires you to change to this in the setup file: extra_compile_args={'gcc': ['/Qstd=c99']}
- move eval.py from legacy
Get tensorboard running cd C:/river_plastic_detection/object_detection tensorboard --logdir=training --host=127.0.0.1 http://127.0.0.1:6006 in browser
Terminate process:
ctrl + C in the running cmd
Copyright (c) [2020] [The Ocean Cleanup]
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