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fartashf avatar fartashf commented on June 14, 2024 1

The 1K results we reported on the result of cross-validation over 5 folds of the test set. Similar to prior works and their open-source codes, we chose 5-folds sequentially without random permutation.

For implementation see here:

vsepp/evaluation.py

Lines 169 to 189 in 226688a

else:
# 5fold cross-validation, only for MSCOCO
results = []
for i in range(5):
r, rt0 = i2t(img_embs[i * 5000:(i + 1) * 5000],
cap_embs[i * 5000:(i + 1) *
5000], measure=opt.measure,
return_ranks=True)
print("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % r)
ri, rti0 = t2i(img_embs[i * 5000:(i + 1) * 5000],
cap_embs[i * 5000:(i + 1) *
5000], measure=opt.measure,
return_ranks=True)
if i == 0:
rt, rti = rt0, rti0
print("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % ri)
ar = (r[0] + r[1] + r[2]) / 3
ari = (ri[0] + ri[1] + ri[2]) / 3
rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2]
print("rsum: %.1f ar: %.1f ari: %.1f" % (rsum, ar, ari))
results += [list(r) + list(ri) + [ar, ari, rsum]]

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chinmay5 avatar chinmay5 commented on June 14, 2024

Just to confirm, we evaluate on the 5 folds and report the best result on one of these samples(the best performing 1k sample) or we report the average value for all 5 sets?

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fartashf avatar fartashf commented on June 14, 2024

See #26

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