Mask R-CNN on NYUv2 This repository mainly contains information from the execution of the Mask R-CNN network [1] on images from the NYUv2 dataset [2] as well as additional metadata. It was created for analyzing the output of Mask R-CNN and post-processing it using contextual information for improving its performance. This work has been carried out by Dr. Jose-Raul Ruiz-Sarmiento (MAPIR group, University of Málaga) and Dr. Shuda Li (AVG group, University of Oxford) in the scope of the European project MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder (Ref: 732158). Concretely, this repository includes: - metadata: + coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2. + coco_object_categories.txt: Object categories considered in COCO dataset. + nyu_object_categories.txt: Object categories used in NYUv2 dataset. + nyu_scene_categories.txt: Scene categories considered in NYUv2. + objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects. - nyu_content: + masks_in_X (Where X is the image index) - Y.png: Where Y is the object index in the image, represents the binary mask of that object. - pixels_labelled.png: Binary mask indicating the labelled pixels in image X. + bboxesX.txt: Where X is the image index, includes the ground truth bounding boxes of the objects in it. Format is: min_x min_y max_x max_y. - preds: + X: Where X is the image index. - Y.png: Where Y is the object index in the image, as detected by Mask R-CNN. Binary image containing the mask of such detected object. + X.txt: Where X is the image index. File containing the objects detected by Mask R-CNN, including: idx class score min_x min_y max_x max_y masks_file, being min_x min_y max_x and max_y bounding box information, while masks_file refers to X/Y.png as described above. + result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3. + gt_iou_X: Where X is the image index. - Y: Where Y is the index of the detected object. + Z.png Where Z is the index of the object in the ground truth. Image showing the masks of both objects, Y and Z, for visually checking their overlapping. - Y.txt: Where Y is the index of the detected object. File containing: + The intersection ratio of the object mask Y with the labelled part of the image. + The IoU value for the mask of object Y and those of ground truth objects. References: [1] He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017. [2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. "Indoor segmentation and support inference from rgbd images." In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.

Work partially supported by a postdoc contract from the I-PPIT-UMA program, financed by the University of Malaga