Comments (3)
I fixed this problem by modify a few lines here
bug version(original):
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
im2show = vis_detections(im2show, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
after fixed:
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
fg = int(j > 0) # class should be agnostic
cls_boxes = boxes[inds, fg * 4:(fg + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
im2show = vis_detections(im2show, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
hope this helpful
from pytorch_rfcn.
Hi! thanks for reply, what is your result of the your model @princewang1994
from pytorch_rfcn.
I got about 0.36 mAP by running train.py directly, it seems that R-FCN's structure in these repo is not the same as that in original paper such as the size of feature map in resnet stage-5. After enlarge the size of stage-5, mAP reached about 49.5, which is also poor ==.
from pytorch_rfcn.
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from pytorch_rfcn.