Comments (1)
I am very confused that labels of query images are indices of list. In the script test_cars196.py, i get labels of query images from test_8131.txt instead of test_label.dat. Then the results of recall@K seem normal.
Using the model author trained, Recall @k on CARS196 test set as follow
| Recall@K(%)
| 1 | 2 | 4 | 8 | 16 | 32
HDC+Contrastive-1-128 | 48.1 | 60.9 | 72.4 | 82.1 | 89.7 | 94.4
HDC+Contrastive-2-128 | 59.0 | 71.3 | 81.0 | 87.7 | 93.0 | 96.2
HDC+Contrastive-3-128 | 65.9 | 77.1 | 85.9 | 92.0 | 95.6 | 97.7
HDC+Contrastive-384 | 72.1 | 82.3 | 88.9 | 93.5 | 96.6 | 98.3
there is little difference.
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Related Issues (15)
- About the train batch size HOT 3
- Why do you change initial GoogleNet loss1 output from 1000 to 128? Also,why do you change the lr_mult simultaneously ? HOT 3
- Would operation system differ the result?Windows or linux? HOT 2
- Issues when extracting features HOT 1
- How to extract feature HOT 2
- recall calculation
- VehicleID Evaluation
- About test CARS196 HOT 4
- Test result does not match the paper HOT 5
- my trained model cannot achieve the same performance compared with the provided trained model HOT 6
- about the cars data HOT 1
- about the negative sample mining process, cascade or fixed? HOT 1
- what‘s message normalization_layer?
- warning: ‘auto’ changes meaning in C++11; please remove it [-Wc++0x-compat] HOT 1
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