The tooth numbering module classifies and numbering dental objects detected as a result of segmentation according to the FDI notation used universally by dentists.
Track your Instance Segmentation model predictions with DeepSortMask. This repository explains how to use deep_sort to track masked objects from MaskRCNN.
Use of Deep Learning to perform Instance Segmentation
Mask RCNN Implementation on Custom Data(Labelme)
mask rcnn training with coco-like dataset. You can use for trainnig your own coco.json (polygon) dataset in Google Colab.
Real-time object detection & tracking pipeline — Mask R-CNN + SORT algorithm with Kalman filtering. 78.4% tracking accuracy, 100% ID stability. Self-supervised evaluation metrics.
Objects detection using Mask-RCNN Architecture
Plot delineation using Unet, Mask-RCCN, and Smoothly Blending Patches Algorithm
Successfully developed a Mask R-CNN-based instance segmentation model to detect and segment various types of road lane markings from complex street images.
Frequency Self-Attention for Building Segmentation in Aerial Imagery. Frequency-domain self-attention for building segmentation in aerial imagery. FsaNet in a Mask R-CNN, attending over 256 DCT coefficients instead of 65,536 pixels. Cheaper and more accurate than the spatial baseline.
Basics and Hand-On
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