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Mask R-CNN on NYUv2

Mask R-CNN on NYUv2 This repository mainly contains information from the execution of the <a href="https://github.com/facebookresearch/maskrcnn-benchmark">Mask R-CNN network </a>[1] on images from the <a href="https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html">NYUv2 dataset</a> [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 <a href="http://mapir.isa.uma.es/jotaraul">Dr. Jose-Raul Ruiz-Sarmiento</a> (MAPIR group, University of Málaga) and <a href="https://lishuda.wordpress.com/">Dr. Shuda Li</a> (AVG group, University of Oxford) in the scope of the European project<a href="http://www.movecare-project.eu/"> MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder</a> (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.

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 12
Reuse readiness: 8
Engagement: 8

Topics

Mask R-CNN NYUv2 Object detection MoveCare