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| author | nunzip <np.scarh@gmail.com> | 2018-12-11 19:24:56 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-12-11 19:24:56 +0000 |
| commit | b3710334107278ded9689afe775bb9d0eff3996a (patch) | |
| tree | 80afd3b3836c4f97f2cad90da4e33b02ab34116f | |
| parent | b1c5d8a82395a8545794b0ce22cccb16ca380647 (diff) | |
| download | vz215_np1915-b3710334107278ded9689afe775bb9d0eff3996a.tar.gz vz215_np1915-b3710334107278ded9689afe775bb9d0eff3996a.tar.bz2 vz215_np1915-b3710334107278ded9689afe775bb9d0eff3996a.zip | |
Revert mAP to interpolate
| -rwxr-xr-x | evaluate.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/evaluate.py b/evaluate.py index 642116f..696356e 100755 --- a/evaluate.py +++ b/evaluate.py @@ -158,8 +158,8 @@ 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 target_pred |