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# FastReID Model Zoo and Baselines
## Introduction
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This file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA V100 GPU.
The software in use were PyTorch 1.6, CUDA 10.1.
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In addition to these official baseline models, you can find more models in [projects/ ](https://github.com/JDAI-CV/fast-reid/tree/master/projects ).
### How to Read the Tables
- The "Name" column contains a link to the config file.
Running `tools/train_net.py` with this config file and 1 GPU will reproduce the model.
### Common Settings for all Person reid models
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**BoT**:
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[Bag of Tricks and A Strong Baseline for Deep Person Re-identification ](http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf ). CVPRW2019, Oral.
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**AGW**:
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[ReID-Survey with a Powerful AGW Baseline ](https://github.com/mangye16/ReID-Survey ).
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**MGN**:
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[Learning Discriminative Features with Multiple Granularities for Person Re-Identification ](https://arxiv.org/abs/1804.01438v1 )
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**SBS**:
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stronger baseline on top of BoT:
Bag of Freebies(BoF):
1. Circle loss
2. Freeze backbone training
3. Cutout data augmentation & Auto Augmentation
4. Cosine annealing learning rate decay
5. Soft margin triplet loss
Bag of Specials(BoS):
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1. Non-local block
2. GeM pooling
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### Market1501 Baselines
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---: |
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| [BoT(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50.yml ) | ImageNet | 94.4% | 86.1% | 59.4% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50.pth ) |
| [BoT(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50-ibn.yml ) | ImageNet | 94.9% | 87.6% | 64.1% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50-ibn.pth ) |
| [BoT(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_S50.yml ) | ImageNet | 95.2% | 88.7% | 66.9% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_S50.pth ) |
| [BoT(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R101-ibn.yml ) | ImageNet| 95.4% | 88.9% | 67.4% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R101-ibn.pth ) |
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: |:---: |
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| [AGW(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50.yml ) | ImageNet | 95.3% | 88.2% | 66.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50.pth ) |
| [AGW(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50-ibn.yml ) | ImageNet | 95.1% | 88.7% | 67.1% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50-ibn.pth ) |
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| [AGW(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml ) | ImageNet | 95.3% | 89.3% | 68.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_S50.pth ) |
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| [AGW(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R101-ibn.yml ) | ImageNet | 95.5% | 89.5% | 69.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R101-ibn.pth ) |
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: |:---:|
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| [SBS(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50.yml ) | ImageNet | 95.4% | 88.2% | 64.8% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50.pth ) |
| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50-ibn.yml ) | ImageNet | 95.7% | 89.3% | 67.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50-ibn.pth ) |
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| [SBS(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_S50.yml ) | ImageNet | 95.8% | 89.4% | 67.6% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_S50.pth ) |
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| [SBS(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R101-ibn.yml ) | ImageNet | 96.3% | 90.3% | 70.0% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R101-ibn.pth ) |
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/mgn_R50-ibn.yml ) | ImageNet | 95.8% | 89.8% | 67.7% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_mgn_R50-ibn.pth ) |
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### DukeMTMC Baseline
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---: |
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| [BoT(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50.yml ) | ImageNet | 87.2% | 77.0% | 42.1% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50.pth ) |
| [BoT(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50-ibn.yml ) | ImageNet | 89.3% | 79.6% | 45.2% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50-ibn.pth ) |
| [BoT(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_S50.yml ) | ImageNet | 90.0% | 80.13% | 45.8% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_S50.pth ) |
| [BoT(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R101-ibn.yml ) | ImageNet| 91.2% | 81.2% | 47.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R101-ibn.pth ) |
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [AGW(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50.yml ) | ImageNet | 89.0% | 79.9% | 46.1% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50.pth ) |
| [AGW(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50-ibn.yml ) | ImageNet | 90.5% | 80.8% | 47.6% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50-ibn.pth ) |
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| [AGW(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_S50.yml ) | ImageNet | 90.9% | 82.4% | 49.2% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_S50.pth ) |
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| [AGW(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R101-ibn.yml ) | ImageNet | 91.7% | 82.3% | 50.0% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R101-ibn.pth ) |
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50.yml ) | ImageNet | 90.3% | 80.3% | 46.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50.pth ) |
| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50-ibn.yml ) | ImageNet | 90.8% | 81.2% | 47.0% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50-ibn.pth ) |
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| [SBS(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_S50.yml ) | ImageNet | 91.0% | 81.4% | 47.6% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_S50.pth ) |
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| [SBS(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R101-ibn.yml ) | ImageNet | 91.9% | 83.6% | 51.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R101-ibn.pth ) |
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/mgn_R50-ibn.yml ) | ImageNet | 91.1% | 82.0% | 46.8% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_mgn_R50-ibn.pth ) |
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### MSMT17 Baseline
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [BoT(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50.yml ) | ImageNet | 74.1% | 50.2% | 10.4% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50.pth ) |
| [BoT(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50-ibn.yml ) | ImageNet | 77.0% | 54.4% | 12.5% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50-ibn.pth ) |
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| [BoT(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_S50.yml ) | ImageNet | 80.8% | 59.9% | 16.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_S50.pth ) |
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| [BoT(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R101-ibn.yml ) | ImageNet| 81.0% | 59.4% | 15.6% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R101-ibn.pth ) |
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [AGW(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50.yml ) | ImageNet | 78.3% | 55.6% | 12.9% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50.pth ) |
| [AGW(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50-ibn.yml ) | ImageNet | 81.2% | 59.7% | 15.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50-ibn.pth ) |
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| [AGW(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_S50.yml ) | ImageNet | 82.6% | 62.6% | 17.7% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_S50.pth ) |
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| [AGW(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R101-ibn.yml ) | ImageNet | 82.0% | 61.4% | 17.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R101-ibn.pth ) |
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50.yml ) | ImageNet | 81.8% | 58.4% | 13.9% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50.pth ) |
| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50-ibn.yml ) | ImageNet | 83.9% | 60.6% | 15.2% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50-ibn.pth ) |
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| [SBS(S50) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_S50.yml ) | ImageNet | 84.1% | 61.7% | 15.2% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_S50.pth ) |
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| [SBS(R101-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R101-ibn.yml ) | ImageNet | 84.8% | 62.8% | 16.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R101-ibn.pth ) |
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/mgn_R50-ibn.yml ) | ImageNet | 85.1% | 65.4% | 18.4% | - |
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### VeRi Baseline
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50-ibn) ](https://github.com/JDAI-CV/fast-reid/blob/master/configs/VeRi/sbs_R50-ibn.yml ) | ImageNet | 97.0% | 81.9% | 46.3% | [model ](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veri_sbs_R50-ibn.pth ) |
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### VehicleID Baseline
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**BoT**:
Test protocol: 10-fold cross-validation; trained on 4 NVIDIA P40 GPU.
< table >
< thead >
< tr >
< th rowspan = "3" align = "center" > Method< / th >
< th rowspan = "3" align = "center" > Pretrained< / th >
< th colspan = "6" align = "center" > Testset size< / th >
< th rowspan = "3" align = "center" > download< / th >
< / tr >
< tr >
< td colspan = "2" align = "center" > Small< / td >
< td colspan = "2" align = "center" > Medium< / td >
< td colspan = "2" align = "center" > Large< / td >
< / tr >
< tr >
< td align = "center" > Rank@1< / td >
< td align = "center" > Rank@5< / td >
< td align = "center" > Rank@1< / td >
< td align = "center" > Rank@5< / td >
< td align = "center" > Rank@1< / td >
< td align = "center" > Rank@5< / td >
< / tr >
< / thead >
< tbody >
< tr >
< td nowrap align = "center" > < a href = "https://github.com/JDAI-CV/fast-reid/blob/master/configs/VehicleID/bagtricks_R50-ibn.yml" > BoT(R50-ibn)< / a > < / td >
< td align = "center" > ImageNet< / td >
< td align = "center" > 86.6%< / td >
< td align = "center" > 97.9%< / td >
< td align = "center" > 82.9%< / td >
< td align = "center" > 96.0%< / td >
< td align = "center" > 80.6%< / td >
< td align = "center" > 93.9%< / td >
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< td align = "center" > < a href = "https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/vehicleid_bot_R50-ibn.pth" > model< / a > < / td >
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< / tr >
< / tbody >
< / table >
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### VERI-Wild Baseline
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**BoT**:
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Test protocol: Trained on 4 NVIDIA P40 GPU.
< table >
< thead >
< tr >
< th rowspan = "3" align = "center" > Method< / th >
< th rowspan = "3" align = "center" > Pretrained< / th >
< th colspan = "9" align = "center" > Testset size< / th >
< th rowspan = "3" align = "center" > download< / th >
< / tr >
< tr >
< td colspan = "3" align = "center" > Small< / td >
< td colspan = "3" align = "center" > Medium< / td >
< td colspan = "3" align = "center" > Large< / td >
< / tr >
< tr >
< td align = "center" > Rank@1< / td >
< td align = "center" > mAP< / td >
< td align = "center" > mINP< / td >
< td align = "center" > Rank@1< / td >
< td align = "center" > mAP< / td >
< td align = "center" > mINP< / td >
< td align = "center" > Rank@1< / td >
< td align = "center" > mAP< / td >
< td align = "center" > mINP< / td >
< / tr >
< / thead >
< tbody >
< tr >
< td nowrap align = "center" > < a href = "https://github.com/JDAI-CV/fast-reid/blob/master/configs/VERIWild/bagtricks_R50-ibn.yml" > BoT(R50-ibn)< / a > < / td >
< td align = "center" > ImageNet< / td >
< td align = "center" > 96.4%< / td >
< td align = "center" > 87.7%< / td >
< td align = "center" > 69.2%< / td >
< td align = "center" > 95.1%< / td >
< td align = "center" > 83.5%< / td >
< td align = "center" > 61.2%< / td >
< td align = "center" > 92.5%< / td >
< td align = "center" > 77.3%< / td >
< td align = "center" > 49.8%< / td >
2021-01-27 17:23:41 +08:00
< td align = "center" > < a href = "https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veriwild_bot_R50-ibn.pth" > model< / a > < / td >
2020-05-29 18:51:02 +08:00
< / tr >
< / tbody >
< / table >
