mask_rcnn_inception_v2_coco

Use Case and High-Level Description

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

Example

Specification

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

Accuracy

Metric Value
coco_orig_precision 27.1199%
coco_orig_segm_precision 21.4805%

Performance

Input

Original Model

Image, name: image_tensor, shape: [1x800x1365x3], format: [BxHxWxC], 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: [1x3x800x1365], format: [BxCxHxW], where:
    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width

Expected color order: BGR.

  1. Information about input image size, name: image_info, shape: [1x3], format: [BxC], 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 the Microsoft* 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: [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: [N, 90, 15, 15], where N is the number of detected masks, 90 is the number of classes (the background class excluded).

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-TensorFlow.txt.