mask_rcnn_inception_v2_coco

Use Case and High-Level Description

Mask R-CNN Inception V2 trained on the Common Objects in Context (COCO) dataset. The model is used for object instance segmentation. For details, see a paper.

Specification

Metric Value
Type Instance segmentation
GFlops 54.926
MParams 21.772
Source framework TensorFlow*

Accuracy

Metric Value
coco_orig_precision 27.12%
coco_orig_segm_precision 21.48%

Input

Original Model

Image, name: image_tensor, shape: 1, 800, 1365, 3, format: B, H, W, C, where:

  • B - batch size
  • H - image height
  • W - image width
  • C - number of channels

Expected color order: RGB.

Converted Model

  1. Image, name: image_tensor, shape: 1, 3, 800, 1365, format: B, C, H, W, where:

    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width

    Expected color order: BGR.

  2. Information about input image size, name: image_info, shape: 1, 3, format: B, C, where:
    • B - batch size
    • C - vector of 3 values in format H, W, S, where H is height, W is width, S is a scale factor (usually 1)

Output

Original Model

  1. Classifier, name: detection_classes. Contains predicted bounding boxes classes in a range [1, 91]. The model was trained on Common Objects in Context (COCO) dataset version with 90 categories of objects, 0 class is for background.
  2. Probability, name: detection_scores. Contains probability of detected bounding boxes.
  3. Detection box, name: detection_boxes. Contains detection boxes coordinates in a format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates of the top left corner, (x_max, y_max) are coordinates of the right bottom corner. Coordinates are rescaled to input image size.
  4. Detections number, name: num_detections. Contains the number of predicted detection boxes.
  5. Segmentation mask, name: detection_masks. Contains segmentation heatmaps of detected objects for all classes for every output bounding box.

Converted Model

  1. The array of summary detection information, name: reshape_do_2d, shape: 100, 7 in the format N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:
    - `image_id` - ID of the image in the batch
    - `label` - predicted class ID
    - `conf` - confidence for the predicted class
    - (`x_min`, `y_min`) - coordinates of the top left bounding box corner (coordinates are stored in the normalized format, in range [0, 1])
    - (`x_max`, `y_max`) - coordinates of the bottom right bounding box corner  (coordinates are stored in the normalized format, in range [0, 1])
    
  2. Segmentation heatmaps for all classes for every output bounding box, name: masks, shape: 100, 90, 15, 15 in the format N, 90, 15, 15, where N is the number of detected masks, 90 is the number of classes (the background class excluded).

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TF-Models.txt.