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)