mAP corrected to per-class
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@@ -118,6 +118,7 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
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mAP = AP.mean()
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# Append image mAP to list
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mAPs.append(mAP)
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# Print image mAP and running mean mAP
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print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, np.mean(mAPs)))
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