How to evaluate my trained model on Tensorflow Object Detection API?

Question

I'm trained a model using Tensorflow's Object Detection API, and i see results of evaluation on Tensorboard.

Now i need to run another evaluation-only run using new test data.

I did search on documentation, and other stackoverflow question but i can't find the right way, only the legacy mode, that did not work for me.

How is the right way to do that?

Answered here https://stackoverflow.com/a/52377729/1571008 – Yaroslav Schubert May 22 '19 at 10:03 — Yaroslav Schubert, May 22 '19 at 10:03

score 2 · Answer 1 · answered May 07 '20 at 00:48

You can also used model_main.py to evaluate your model.

If you want to evaluate your model on validation data you should use:

python models/research/object_detection/model_main.py --pipeline_config_path=/path/to/pipeline_file --model_dir=/path/to/output_results --checkpoint_dir=/path/to/directory_holding_checkpoint --run_once=True

If you want to evaluate your model on training data, you should set 'eval_training_data' as True, that is:

python models/research/object_detection/model_main.py --pipeline_config_path=/path/to/pipeline_file --model_dir=/path/to/output_results --eval_training_data=True --checkpoint_dir=/path/to/directory_holding_checkpoint --run_once=True

I also add comments to clarify some of previous options:

--pipeline_config_path: path to "pipeline.config" file used to train detection model. This file should include paths to the TFRecords files (train and test files) that you want to evaluate, i.e. :

    ...
    train_input_reader: {
        tf_record_input_reader {
                #path to the training TFRecord
                input_path: "/path/to/train.record"
        }
        #path to the label map 
        label_map_path: "/path/to/label_map.pbtxt"
    }
    ...
    eval_input_reader: {
        tf_record_input_reader {
            #path to the testing TFRecord
            input_path: "/path/to/test.record"
        }
        #path to the label map 
        label_map_path: "/path/to/label_map.pbtxt"
    }
    ...

--model_dir: Output directory where resulting metrics will be written, particularly "events.*" files that can be read by tensorboard.

--checkpoint_dir: Directory holding a checkpoint. That is the model directory where checkpoint files ("model.ckpt.*") has been written, either during training process, or after export it by using "export_inference_graph.py".

--run_once: True to run just one round of evaluation.

How to evaluate my trained model on Tensorflow Object Detection API?

1 Answers1