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7 changes: 4 additions & 3 deletions utils/autoanchor.py
Original file line number Diff line number Diff line change
Expand Up @@ -57,9 +57,10 @@ def metric(k): # compute metric
anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
check_anchor_order(m)
LOGGER.info(f'{PREFIX}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
s = f'{PREFIX}Done βœ… (optional: update model *.yaml to use these anchors in the future)'
else:
LOGGER.info(f'{PREFIX}Original anchors better than new anchors. Proceeding with original anchors.')
s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
LOGGER.info(emojis(s))


def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
Expand Down Expand Up @@ -120,7 +121,7 @@ def print_results(k, verbose=True):
# Filter
i = (wh0 < 3.0).any(1).sum()
if i:
LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
# wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1

Expand Down