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author | nunzip <np.scarh@gmail.com> | 2018-12-11 19:26:29 +0000 |
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committer | nunzip <np.scarh@gmail.com> | 2018-12-11 19:26:29 +0000 |
commit | c053a23b2fa24269270be346977048b73f69b236 (patch) | |
tree | 16e466eaee10dac520e58ef1487fc253ffaed29f | |
parent | 9c3c0905f162c1e5567c6879f3b326004a1626ee (diff) | |
download | vz215_np1915-c053a23b2fa24269270be346977048b73f69b236.tar.gz vz215_np1915-c053a23b2fa24269270be346977048b73f69b236.tar.bz2 vz215_np1915-c053a23b2fa24269270be346977048b73f69b236.zip |
Revert mAP to interpolate
-rwxr-xr-x | opt.py | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -159,8 +159,8 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))]) #print(mAP[i]) for i in range(probe_label.shape[0]): - #mAP[i] = sum(max_level_precision[i])/11 - mAP[i] = sum(precision[i])/args.neighbors + mAP[i] = sum(max_level_precision[i])/11 + #mAP[i] = sum(precision[i])/args.neighbors print('mAP:',np.mean(mAP)) return np.mean(mAP) |