Mask R-CNN Resnet50 Atrous trained on COCO dataset. It is used for object instance segmentation. For details, see the paper.
Metric | Value |
---|---|
Type | Instance segmentation |
GFlops | 294.738 |
MParams | 50.222 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
coco_orig_precision | 29.7512% |
coco_orig_segm_precision | 27.4597% |
Image, name: image_tensor
, shape: [1x800x1365x3], format: [BxHxWxC], where:
Expected color order: RGB.
image_tensor
, shape: [1x3x800x1365], format: [BxCxHxW], where:Expected color order: BGR.
image_info
, shape: [1x3], format: [BxC], where: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.detection_scores
. Contains probability of detected bounding boxes.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.num_detections
. Contains the number of predicted detection boxes.detection_masks
. Contains segmentation heatmaps of detected objects for all classes for every output bounding box.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 batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box corner (coordinates stored in normalized format, in range [0, 1])x_max
, y_max
) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in range [0, 1])masks
, shape: [N, 90, 33, 33], where N is the number of detected masks, 90 is the number of classes (the background class excluded).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:
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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.