@@ -165,7 +165,7 @@ def test(data, | |||
# Compute statistics | |||
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy | |||
if len(stats): | |||
if len(stats) and stats[0].any(): | |||
p, r, ap, f1, ap_class = ap_per_class(*stats) | |||
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95] | |||
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() |
@@ -416,7 +416,10 @@ class LoadImagesAndLabels(Dataset): # for training/testing | |||
pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( | |||
cache_path, nf, nm, ne, nd, n) | |||
assert nf > 0, 'No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) | |||
if nf == 0: | |||
s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) | |||
print(s) | |||
assert not augment, '%s. Can not train without labels.' % s | |||
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) | |||
self.imgs = [None] * n |