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Mask R-CNN on NYUv2

<p><strong>Mask R-CNN on NYUv2</strong></p> <p>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&nbsp;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 M&aacute;laga) 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).</p> <p>Concretely, <strong>this repository includes</strong>:</p> <p>- metadata:<br> &nbsp;&nbsp; &nbsp;+ coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2.<br> &nbsp;&nbsp; &nbsp;+ coco_object_categories.txt: Object categories considered in COCO dataset.<br> &nbsp;&nbsp; &nbsp;+ nyu_object_categories.txt: Object categories used in NYUv2 dataset.<br> &nbsp;&nbsp; &nbsp;+ nyu_scene_categories.txt: Scene categories considered in NYUv2.<br> &nbsp;&nbsp; &nbsp;+ objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects.</p> <p>- nyu_content:<br> &nbsp;&nbsp; &nbsp;+ masks_in_X (Where X is the image index)<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;- Y.png: Where Y is the object index in the image, represents the binary mask of that object.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;- pixels_labelled.png: Binary mask indicating the labelled pixels in image X.<br> &nbsp;&nbsp; &nbsp;+ 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.</p> <p>- preds:<br> &nbsp;&nbsp; &nbsp;+ X: Where X is the image index.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;- 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.<br> &nbsp;&nbsp; &nbsp;+ 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.<br> &nbsp;&nbsp; &nbsp;+ result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3.<br> &nbsp;&nbsp; &nbsp;+ gt_iou_X: Where X is the image index.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;- Y: Where Y is the index of the detected object.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+ 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.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;- Y.txt: Where Y is the index of the detected object. File containing:<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+ The intersection ratio of the object mask Y with the labelled part of the image.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+ The IoU value for the mask of object Y and those of ground truth objects.<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;<br> <strong>References:</strong></p> <p>[1] He, Kaiming, Georgia Gkioxari, Piotr Doll&aacute;r, and Ross Girshick. &quot;Mask r-cnn.&quot; In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.<br> [2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. &quot;Indoor segmentation and support inference from rgbd images.&quot; In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.</p>