How to evaluate a pretrained model in Tensorflow object detection api

Question

Trying work with the recently released Tensorflow Object Detection API, and was wondering how I could evaluate one of the pretrained models they provided in their model zoo? ex.

Answer 1

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". In your case, you should point to the pretrained model folder download from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md.

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

Answer 2

You can evaluate the pretrained models by running the eval.py script. It will ask you to point to a config file (which will be in the samples/configs directory) and a checkpoint, and for this you will provide a path of the form .../.../model.ckpt (dropping any extensions, like .meta, or .data-00000-of-00001).

You also have to create a file named "checkpoint" inside the directory that contains that checkpoint that you'd like to evaluate. Then inside that file write the following two lines:

model_checkpoint_path: “path/to/model.ckpt"
all_model_checkpoint_paths: “path/to/model.ckpt"

(where you modify path/to/ appropriately)

The number that you get at the end is mean Average Precision using 50% IOU as the cutoff threshold for true positives. This is slightly different than the metric that is reported in the model zoo, which uses the COCO mAP metric and averages over multiple IOU values.

Answer 3

Try:

python eval.py --logtostderr --checkpoint_dir=training --eval_dir=path/to/eval_dir --pipeline_config_path=path/to/pretrained_model.config

For example:

python eval.py --logtostderr --checkpoint_dir=training --eval_dir=images/val \
  --pipelineline_config_path=training/faster_rcnn_inception_v2.config

Note:

The training dir contains all your training checkpoints. During training Tensorflow generates a checkpoint file inside this directory with all your checkpoint metadata in it so you do not need to create another one. If you wish to evaluate your trained custom model after generating your inference graph then ensure your change your original pretrained_model/model.chpt to your new_trained_model/model.ckpt in the .config you used for training. You should get a similar output:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.457
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.729
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.502
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.659
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.398
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.559
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.590
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.236
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.486
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
INFO:tensorflow:Writing metrics to tf summary.
INFO:tensorflow:DetectionBoxes_Precision/mAP: 0.456758
INFO:tensorflow:DetectionBoxes_Precision/mAP (large): 0.659280
INFO:tensorflow:DetectionBoxes_Precision/mAP (medium): 0.296693
INFO:tensorflow:DetectionBoxes_Precision/mAP (small): 0.122108
INFO:tensorflow:DetectionBoxes_Precision/mAP@.50IOU: 0.728587
INFO:tensorflow:DetectionBoxes_Precision/mAP@.75IOU: 0.502194
INFO:tensorflow:DetectionBoxes_Recall/AR@1: 0.397509
INFO:tensorflow:DetectionBoxes_Recall/AR@10: 0.558966
INFO:tensorflow:DetectionBoxes_Recall/AR@100: 0.590182
INFO:tensorflow:DetectionBoxes_Recall/AR@100 (large): 0.745691
INFO:tensorflow:DetectionBoxes_Recall/AR@100 (medium): 0.485964
INFO:tensorflow:DetectionBoxes_Recall/AR@100 (small): 0.236275
INFO:tensorflow:Losses/Loss/BoxClassifierLoss/classification_loss: 0.234645
INFO:tensorflow:Losses/Loss/BoxClassifierLoss/localization_loss: 0.139109
INFO:tensorflow:Losses/Loss/RPNLoss/localization_loss: 0.603733
INFO:tensorflow:Losses/Loss/RPNLoss/objectness_loss: 0.206419