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@ -4,7 +4,7 @@
### This code has the source code for the paper "Re-ranking Person Re-identification with k-reciprocal Encoding". Including:
1. IDE baseline
2. Re-ranking code
3. The CUHK03 new training/testing protocol
3. CUHK03 new training/testing protocol
If you find this code useful in your research, please consider citing:
@ -101,14 +101,16 @@ You can download our pre-trained IDE models and IDE features, and put them in th
- IDE features [[Baiduyun]](https://pan.baidu.com/s/1c1TtKcw) [[Google drive]](https://drive.google.com/open?id=0B7TOZKXmIjU3ODhaRm8yN2QzRHc)
Using the above IDE models and IDE features, you can reproduce the results as follows:
Using the above IDE models and IDE features, you can reproduce the results with our re-ranking method as follows:
- Market-1501
|Methods |   Rank@1 | mAP|
| -------- | ----- | ---- |
|IDE_ResNet_50 + Euclidean | 78.92% | 55.03%|
|IDE_ResNet_50 + Euclidean + re-ranking | 81.44% | 70.39%|
|IDE_ResNet_50 + XQDA | 77.58% | 56.06%|
|IDE_ResNet_50 + XQDA + re-ranking | 80.70% | 69.98%|
For Market-1501, these results are better than those reported in our paper, since we add a dropout = 0.5 layer after pool5.
@ -118,10 +120,15 @@ For Market-1501, these results are better than those reported in our paper, sinc
| -------| ----- | ---- |---- |---- |
|Methods | Rank@1 | mAP| Rank@1 | mAP|
|LOMO + XQDA | 14.8% | 13.6%|12.8% | 11.5%|
|LOMO + XQDA + re-ranking | 19.1% | 20.8%|16.6% | 17.8%|
|IDE_CaffeNet + Euclidean | 15.6% | 14.9%| 15.1% | 14.2%|
|IDE_CaffeNet + Euclidean + re-ranking | 19.1% | 21.3%|19.3% | 20.6%|
|IDE_CaffeNet + XQDA | 21.9% | 20.0%|21.1% | 19.0%|
|IDE_CaffeNet + XQDA + re-ranking | 25.9% | 27.8%|26.4% | 26.9%|
|IDE_ResNet_50 + Euclidean | 22.2% | 21.0%|21.3% | 19.7%|
|IDE_ResNet_50 + Euclidean + re-ranking | 26.6% | 28.9%|24.9% | 27.3%|
|IDE_ResNet_50 + XQDA | 32.0% | 29.6%|31.1% | 28.2%|
|IDE_ResNet_50 + XQDA + re-ranking | 38.1% | 40.3%|34.7% | 37.4%|
### Contact us