def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'val2017' args.rpn_anchor_scales = (2, 4, 8, 16, 32) args.rcnn_num_classes = len(coco.classes) return coco(args.imageset, 'data', 'data/coco')
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'train2017' args.rcnn_num_classes = len(coco.classes) isets = args.imageset.split('+') roidb = [] for iset in isets: imdb = coco(iset, 'data', 'data/coco') imdb.filter_roidb() imdb.append_flipped_images() roidb.extend(imdb.roidb) return roidb
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'train2017' args.rcnn_num_classes = len(coco.classes) isets = args.imageset.split('+') roidb = [] for iset in isets: imdb = coco(iset, 'data', 'data/coco') imdb.filter_roidb() imdb.append_flipped_images() roidb.extend(imdb.roidb) return roidb
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'val2017' args.rcnn_num_classes = len(coco.classes) return coco(args.imageset, 'data', 'data/coco')
from imdb.coco import coco imdb = coco('person_val2017', 'data', '/mnt/data/coco') detections = pickle.load(open('./data/cache/coco_person_val2017_detections.pkl', 'rb')) imdb._write_coco_results(detections) imdb._do_python_eval() Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.361 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.618 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.165 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.420 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.526 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'val2017' args.rcnn_num_classes = len(coco.classes) return coco(args.imageset, 'data', 'data/coco')
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'val2017' return coco(args.imageset, 'data', '/mnt/data/coco')
def get_coco(args): from imdb.coco import coco if not args.imageset: args.imageset = 'val2017' return coco(args.imageset, 'data', args.data_path)