Validation Application

Inference Engine Validation Application is a tool that allows to infer deep learning models with standard inputs and outputs configuration and to collect simple validation metrics for topologies. It supports top-1 and top-5 metric for Classification networks and 11-points mAP metric for Object Detection networks.

NOTE: Before running the application with trained models, make sure the models are converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

Possible use cases of the tool:

Validation Application Options

The Validation Application provides the following command-line interface (CLI):

Usage: validation_app [OPTION]
Available options:
-h Print a help message
-t <type> Type of an inferred network ("C" by default)
-t "C" for classification
-t "OD" for object detection
-i <path> Required. Folder with validation images. Path to a directory with validation images. For Classification models, the directory must contain folders named as labels with images inside or a .txt file with a list of images. For Object Detection models, the dataset must be in VOC format.
-m <path> Required. Path to an .xml file with a trained model
-lbl <path> Labels file path. The labels file contains names of the dataset classes
-l <absolute_path> Required for CPU custom layers. Absolute path to a shared library with the kernel implementations
-c <absolute_path> Required for GPU custom kernels.Absolute path to an .xml file with the kernel descriptions.
-d <device> Target device to infer on: CPU (default), GPU, FPGA, or MYRIAD. The application looks for a suitable plugin for the specified device.
-b N Batch size value. If not specified, the batch size value is taken from IR
-ppType <type> Preprocessing type. Options: "None", "Resize", "ResizeCrop"
-ppSize N Preprocessing size (used with ppType="ResizeCrop")
-ppWidth W Preprocessing width (overrides -ppSize, used with ppType="ResizeCrop")
-ppHeight H Preprocessing height (overrides -ppSize, used with ppType="ResizeCrop")
--dump Dump file names and inference results to a .csv file
Classification-specific options:
-Czb true "Zero is a background" flag. Some networks are trained with a modified dataset where the class IDs are enumerated from 1, but 0 is an undefined "background" class (which is never detected)
Object detection-specific options:
-ODkind <kind> Type of an Object Detection model. Options: SSD
-ODa <path> Required for Object Detection models. Path to a directory containing an .xml file with annotations for images.
-ODc <file> Required for Object Detection models. Path to a file containing a list of classes
-ODsubdir <name> Directory between the path to images (specified with -i) and image name (specified in the .xml file). For VOC2007 dataset, use JPEGImages.

The tool options are divided into two categories:

  1. Common options named with a single letter or a word, such as -b or --dump. These options are the same in all Validation Application modes.
  2. Network type-specific options named as an acronym of the network type (C or OD) followed by a letter or a word.

General Workflow

When executed, the Validation Application perform the following steps:

  1. Loads a model to an Inference Engine plugin
  2. Reads validation set (specified with the -i option):
    • if you specified a directory, the application tries to load labels first. To do this, it searches for the file with the same name as a model, but with .labels extension (instead of .xml). Then it searches for the specified folder, detects its sub-folders named as known labels, and adds all images from these sub-folders to the validation set. When there are no such sub-folders, validation set is considered empty.
    • if you specified a .txt file, the application reads this file expecting every line to be in the correct format. For more information about the format, refer to the Preparing the Dataset section below.
  3. Reads the batch size value specified with the -b option and loads this number of images to the plugin

    NOTE: Images loading time is not a part of inference time reported by the application.

  4. The plugin infers the model, and the Validation Application collects the statistics.

You can also retrieve infer result by specifying the --dump option, however it generates a report only for Classification models. This CLI option enables creation (if possible) of an inference report in the .csv format.

The structure of the report is a set of lines, each of them contains semicolon-separated values:

Prepare a Dataset

You must prepare the dataset before running the Validation Application. The format of dataset depends on a type of the model you are going to validate. Make sure that the dataset is format is applicable for the chosen model type.

Dataset Format for Classification: Folders as Classes

In this case, a dataset has the following structure:

|-- <path>/dataset
|-- apron
|-- apron1.bmp
|-- apron2.bmp
|-- collie
|-- a_big_dog.jpg
|-- coral reef
|-- reef.bmp
|-- Siamese
|-- cat3.jpg

This structure means that each folder in dataset directory must have the name of one of the classes and contain all images of this class. In the given example, there are two images that represent the class apron, while three other classes have only one image each.

NOTE: A dataset can contain images of both .bmp and .jpg formats.

The correct way to use such dataset is to specify the path as -i <path>/dataset.

Dataset Format for Classification: List of Images (ImageNet-like)

If you want to use this dataset format, create a single file with a list of images. In this case, the correct set of files must be similar to the following:

|-- <path>/dataset
|-- apron1.bmp
|-- apron2.bmp
|-- a_big_dog.jpg
|-- reef.bmp
|-- cat3.jpg
|-- labels.txt

Where labels.txt looks like:

apron1.bmp 411
apron2.bmp 411
cat3.jpg 284
reef.bmp 973
a_big_dog.jpg 231

Each line of the file must contain the name of the image and the ID of the class that it represents in the format <image_name> tabulation <class_id>. For example, apron1.bmp represents the class with ID 411.

NOTE: A dataset can contain images of both .bmp and .jpg formats.

The correct way to use such dataset is to specify the path as -i <path>/dataset/labels.txt.

Dataset Format for Object Detection (VOC-like)

Object Detection SSD models can be inferred on the original dataset that was used as a testing dataset during the model training. To prepare the VOC dataset, follow the steps below :

  1. Download the pre-trained SSD-300 model from the SSD GitHub* repository at
  2. Download VOC2007 testing dataset:
    tar -xvf VOCtest_06-Nov-2007.tar
  3. Convert the model with the Model Optimizer.
  4. Create a proper .txt class file from the original labelmap_voc.prototxt. The new file must be in the following format:
    none_of_the_above 0
    aeroplane 1
    bicycle 2
    bird 3
    boat 4
    bottle 5
    bus 6
    car 7
    cat 8
    chair 9
    cow 10
    diningtable 11
    dog 12
    horse 13
    motorbike 14
    person 15
    pottedplant 16
    sheep 17
    sofa 18
    train 19
    tvmonitor 20
    Save this file as VOC_SSD_Classes.txt.

Validate Classification Models

Once you have prepared the dataset (refer to the Preparing the Dataset section above), run the following command to infer a classification model on the selected dataset:

./validation_app -t C -i <path_to_images_directory_or_txt_file> -m <path_to_classification_model>/<model_name>.xml -d <CPU|GPU>

Validate Object Detection Models

NOTE: Validation Application was validated with SSD CNN. Any network that can be inferred by the Inference Engine and has the same input and output format as one of these should be supported as well.

Once you have prepared the dataset (refer to the Preparing the Dataset section above), run the following command to infer an Object Detection model on the selected dataset:

./validation_app -d CPU -t OD -ODa "<path_to_VOC_dataset>/VOCdevkit/VOC2007/Annotations" -i "<path_to_VOC_dataset>/VOCdevkit" -m "<path_to_model>/vgg_voc0712_ssd_300x300.xml" -ODc "<path_to_classes_file>/VOC_SSD_Classes.txt" -ODsubdir JPEGImages

Understand Validation Application Output

During the validation process, you can see the interactive progress bar that represents the current validation stage. When it is full, the validation process is over, and you can analyze the output.

Key data from the output:

Below you can find the example output for Classification models, which reports average infer time and Top-1 and Top-5 metric values:

Average infer time (ms): 588.977 (16.98 images per second with batch size = 10)
Top1 accuracy: 70.00% (7 of 10 images were detected correctly, top class is correct)
Top5 accuracy: 80.00% (8 of 10 images were detected correctly, top five classes contain required class)

Below you can find the example output for Object Detection models:

Progress: [....................] 100.00% done
[ INFO ] Processing output blobs
Network load time: 27.70ms
Model: /home/user/models/ssd/withmean/vgg_voc0712_ssd_300x300/vgg_voc0712_ssd_300x300.xml
Model Precision: FP32
Batch size: 1
Validation dataset: /home/user/Data/SSD-data/testonly/VOCdevkit
Validation approach: Object detection network
Average infer time (ms): 166.49 (6.01 images per second with batch size = 1)
Average precision per class table:
Class AP
1 0.796
2 0.839
3 0.759
4 0.695
5 0.508
6 0.867
7 0.861
8 0.886
9 0.602
10 0.822
11 0.768
12 0.861
13 0.874
14 0.842
15 0.797
16 0.526
17 0.792
18 0.795
19 0.873
20 0.773
Mean Average Precision (mAP): 0.7767

This output shows the resulting mAP metric value for the SSD300 model used to prepare the dataset. This value repeats the result stated in the SSD GitHub* repository and in the original arXiv paper.

See Also