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Question: computer graphics course make dataset for the following...

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Computer graphics course
Make dataset for the following

Normal No SpacingHeading 1Heading 2Heading 3 Heading 4 Text Box In this competition, you will predict segmentations of different movable objects appearing in the view of a car camera. This dataset contains a large number of segmented and original driving images. There are multiple labels: car, 33 motorbicycle, 34 bicycle, 35 person, 36 rider, 37 truck, 38 bus, 39 tricycle, 40 others, 0 rover, 1 sky, 17 car groups, 161 motorbicycle group, 162 bicycle _group, 163 person group, 164 rider group, 165 truck group, 166 bus group, 167 tricycle group, 168 road, 49 siderwalk, 50 traffic_cone, 65 road pile, 66 fence, 67 traffic light, 81 pole, 82 traffic sign, 83 wall, 84 dustbin, 85 billboard, 86 building, 97 bridge, 98 tunnel, 99 overpass, 100 vegatation, 113 unlabeled, 255 In this competition, we evaluate seven different instance-level annotations, which are car, motorcycle, bicycle, pedestrian, truck, bus, and tricycle. The corresponding groups, such as car group and bicycle group, are annotated when boundaries cannot be distinguished by labelers. These groups are not evaluated currently Training labels format The training images labels are encoded in a format mixing spatial and label/instance information: 151 of 364L Sec 1 Pages: 1 of1 MacBook Air
a Document2 200%- t Elements Tables harts SmartArt Review Paragraph Normal No SpacingHeading 1Heading 2 Heading 3Heading Training labels format The training images labels are encoded in a format mixing spatial and label/instance information: .All the images are the same size (width, height) of the original images . Pixel values indicate both the label and the instance. Each label could contain multiple object instances. m(PixelValue / 1000) is the label (class of object) PixelValue % 1000 is the instance id For example, a pixel value of 33000 means it belongs to label 33 (a car), is instance #0, while the pixel value of 33001 means it also belongs to class 33 (a car, and is instance #1. These represent two different cars in an image. · . Test submission format The submission should be in Run Length Encoding format. Each submission line should represent one obiect instance. with the following of 1 words: 15101 364 L Print Layout View Sec 1 Pager: 2
A - EEAbDEA.codE. AaßbCcD Adbodt AaBCeDM Kabcoste Normalー」 No Spacing Heading 1 Heading 2- Heading 3- jteadi 4 two ditterent cars in an image. Test submission format The submission should be in Run Length Encoding format. Each submission line should represent one object instance, with the following columns: Imageld,Labelld,Confidence,PixelCount,EncodedPixels where Imageld is the file name, Labelld is the class of that object (car, person, etc), .Confidence is your confidence of the prediction, PixelCount is the total number of pixels in that object (this is to help our evaluation execution speed), .EncodedPixels is Run-length encoded, with each pair delimited by l, for example: 1 310 S implies pixels 1,2,3,10,11,12,13,14 are to be included in the mask. The pixels are zero-indexed and numbered from left to right, then top to bottom. of words 1510f 364 L Print Layout view Sec 1 Pages J. sec 1 Pages:o 1 MacBook Air
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