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cs:vision:object_detection:start [2018/03/30 16:56] James Irwin [Generating Training Data] |
cs:vision:object_detection:start [2018/03/31 18:16] Mike Bykhovtsev Added instructions on how to convert trained data |
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| ====== Train Network ====== | ====== Train Network ====== | ||
| + | Instruction were adapted from [[https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_locally.md | here]]. | ||
| + | |||
| + | ===== Workspace Setup ===== | ||
| + | Setup your workspace with the following directory structure: | ||
| + | object_detection_workspace/ | ||
| + | ├── data | ||
| + | ├── models | ||
| + | └── output | ||
| + | ├── eval | ||
| + | └── train | ||
| + | |||
| + | data/ will hold your training data (label_map.pbtext, train.record, and test.record). output/ and its subdirectories hold the outputs of the training and evaluation programs. models/ will store the various network architectures you're experimenting with (you might just have one model). | ||
| + | |||
| + | ===== Getting a Model ===== | ||
| + | Instructions were adapted from [[https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md | here]]. | ||
| + | One of the advantages of using TFODA is that it is really easy to try different network architectures (models) and seeing their speed vs. accuracy tradeoffs. To get a model | ||
| + | ===== Exporting a trained model for inference ===== | ||
| + | To export checkpoint trained data for ''%%robosub_object_detection%%'' format you need to follow [[https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/exporting_models.md|these]] instructions. Or run this: | ||
| + | python object_detection/export_inference_graph.py \ | ||
| + | --input_type image_tensor \ | ||
| + | --pipeline_config_path ${PIPELINE_CONFIG_PATH} \ | ||
| + | --trained_checkpoint_prefix ${TRAIN_PATH} \ | ||
| + | --output_directory output_inference_graph.pb | ||
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