Customized train/inference pipelines of using TensorFlow object detection API [1] with TensorFlow 2.
Follow the steps in [2].
Modify the model configurations, data loading, data preprocessing,...etc inside each code.
Train:
python train.py
Inference:
python inference.py
Check examples in [3] pasted below.
Train:
# From the tensorflow/models/research/ directory
PIPELINE_CONFIG_PATH={path to pipeline config file}
MODEL_DIR={path to model directory}
python object_detection/model_main_tf2.py \
--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
--model_dir=${MODEL_DIR} \
--alsologtostderr
where ${PIPELINE_CONFIG_PATH} points to the pipeline config and ${MODEL_DIR} points to the directory in which training checkpoints and events will be written.
Evaluation:
# From the tensorflow/models/research/ directory
PIPELINE_CONFIG_PATH={path to pipeline config file}
MODEL_DIR={path to model directory}
CHECKPOINT_DIR=${MODEL_DIR}
python object_detection/model_main_tf2.py \
--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
--model_dir=${MODEL_DIR} \
--checkpoint_dir=${CHECKPOINT_DIR} \
--alsologtostderr
where ${CHECKPOINT_DIR} points to the directory with checkpoints produced by the training job. Evaluation events are written to ${MODEL_DIR/eval}.
[1] https://github.com/tensorflow/models/tree/master/research/object_detection
[2] https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html
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