This model is an instance segmentation network for 80 classes of objects. It is a Mask R-CNN with ResNet50 backbone, FPN and Bottom-Up Augmentation blocks and light-weight RPN.
|MS COCO val2017 box AP||31.27%|
|MS COCO val2017 mask AP||27.83%|
|Max objects to detect||100|
Average Precision (AP) is defined and measured according to standard MS COCO evaluation procedure.
im_data, shape: [1x3x480x480] - An input image in the format [1xCxHxW]. The expected channel order is BGR.
im_info, shape: [1x3] - Image information: processed image height, processed image width and processed image scale w.r.t. the original image resolution.
classes, shape: [100, ] - Contiguous integer class ID for every detected object, '0' for background, i.e. no object.
scores: shape: [100, ] - Detection confidence scores in range [0, 1] for every object.
boxes, shape: [100, 4] - Bounding boxes around every detected objects in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
raw_masks, shape: [100, 81, 28, 28] - Segmentation heatmaps for all classes for every output bounding box.
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