mirror of https://github.com/JDAI-CV/fast-reid.git
update model config url
liaoxingyu
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MODEL_ZOO.md
176
MODEL_ZOO.md
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@ -2,16 +2,32 @@
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## Introduction
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This file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA P40 GPU.
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The software in use were PyTorch 1.4, 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).
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### How to Read the Tables
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- The "Name" column contains a link to the config file.
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Running `tools/train_net.py` with this config file and 1 GPU will reproduce the model.
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- The *model id* column is provided for ease of reference. To check downloaded file integrity, any model on this page contains tis md5 prefix in its file name.
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- Training curves and other statistics can be found in `metrics` for each model.
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### 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. CVPRW2019, Oral.
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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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This is a re-implementation of [ReID-Survey with a Powerful AGW Baseline](https://github.com/mangye16/ReID-Survey)
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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:
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@ -33,136 +49,134 @@ Bag of Specials(BoS):
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BoT(R50) | ImageNet | 94.4% | 86.1% | 59.4% |
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| BoT(R50-ibn) | ImageNet | 94.9% | 87.6% | 64.1% |
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| BoT(S50) | ImageNet | 95.1% | 88.5% | 66.0% |
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| BoT(R101-ibn) | ImageNet| 95.4% | 88.9% | 67.4% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---: |
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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% | - |
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| [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% | - |
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| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_S50.yml) | ImageNet | 95.1% | 88.5% | 66.0% | - |
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| [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% | - |
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW(R50) | ImageNet | 95.3% | 88.2% | 66.3% |
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| AGW(R50-ibn) | ImageNet | 95.1% | 88.7% | 67.1% |
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| AGW(S50) | ImageNet | 94.7% | 87.1% | 62.2% |
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| AGW(R101-ibn) | ImageNet | 95.5% | 89.5% | 69.5% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: |:---: |
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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% | - |
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| [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% | -|
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| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml) | ImageNet | 94.7% | 87.1% | 62.2% | -|
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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% | - |
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50) | ImageNet | 95.4% | 88.2% | 64.8% |
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| SBS(R50-ibn) | ImageNet | 95.7% | 89.3% | 67.5% |
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| SBS(S50) | ImageNet | 95.0% | 87.0% | 60.6% |
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| SBS(R101-ibn) | ImageNet | 96.3% | 90.3% | 70.0% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: |:---:|
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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% | - |
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| [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% | -|
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| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_S50.yml) | ImageNet | 95.0% | 87.0% | 60.6% | -|
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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% | -|
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50-ibn) | ImageNet | 95.8% | 89.7% | 67.0% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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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% | 89.7% | 67.0% | -|
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### DukeMTMC Baseline
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BoT(R50) | ImageNet | 87.1% | 76.9% | 41.6% |
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| BoT(R50-ibn) | ImageNet | 89.6% | 79.1% | 44.4% |
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| BoT(S50) | ImageNet | 87.8% | 77.7% | 39.6% |
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| BoT(R101-ibn) | ImageNet| 91.1% | 81.3% | 47.7% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---: |
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| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50.yml) | ImageNet | 87.1% | 76.9% | 41.6% | - |
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| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50-ibn.yml) | ImageNet | 89.6% | 79.1% | 44.4% | - |
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| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_S50.yml) | ImageNet | 87.8% | 77.7% | 39.6% | - |
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| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R101-ibn.yml) | ImageNet| 91.1% | 81.3% | 47.7% | -|
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW(R50) | ImageNet | 89.0% | 79.9% | 46.3% |
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| AGW(R50-ibn) | ImageNet | 89.8% | 80.7% | 47.7% |
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| AGW(S50) | ImageNet | 89.9% | 79.7% | 44.2% |
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| AGW(R101-ibn) | ImageNet | 91.4% | 82.1% | 50.2% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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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% | 46.3% | - |
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| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50-ibn.yml) | ImageNet | 89.8% | 80.7% | 47.7% | - |
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| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_S50.yml) | ImageNet | 89.9% | 79.7% | 44.2% | -|
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| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R101-ibn.yml) | ImageNet | 91.4% | 82.1% | 50.2% | -|
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50) | ImageNet | 89.6% | 79.8% | 44.6% |
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| SBS(R50-ibn) | ImageNet | 91.3% | 81.6% | 47.6% |
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| SBS(S50) | ImageNet | 90.5% | 79.1% | 42.7% |
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| SBS(R101-ibn) | ImageNet | 92.4% | 83.2% | 49.7% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50.yml) | ImageNet | 89.6% | 79.8% | 44.6% | -|
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| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50-ibn.yml) | ImageNet | 91.3% | 81.6% | 47.6% | -|
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| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_S50.yml) | ImageNet | 90.5% | 79.1% | 42.7% | -|
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| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R101-ibn.yml) | ImageNet | 92.4% | 83.2% | 49.7% | -|
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50-ibn) | ImageNet | 91.6% | 82.1% | 46.7% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/mgn_R50-ibn.yml) | ImageNet | 91.6% | 82.1% | 46.7% | - |
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### MSMT17 Baseline
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**BoT**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BoT(R50) | ImageNet | 72.3% | 48.3% | 9.7% |
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| BoT(R50-ibn) | ImageNet | 77.0% | 54.4% | 12.5% |
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| BoT(S50) | ImageNet | 80.4% | 59.2% | 15.9% |
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| BoT(R101-ibn) | ImageNet| 79.0% | 57.5% | 14.6% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50.yml) | ImageNet | 72.3% | 48.3% | 9.7% | -|
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| [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% | -|
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| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_S50.yml) | ImageNet | 80.4% | 59.2% | 15.9% | -|
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| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R101-ibn.yml) | ImageNet| 79.0% | 57.5% | 14.6% | -|
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**AGW**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW(R50) | ImageNet | 76.7% | 53.6% | 12.2% |
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| AGW(R50-ibn) | ImageNet | 79.3% | 57.5% | 14.3% |
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| AGW(S50) | ImageNet | 77.3% | 54.7% | 12.6% |
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| AGW(R101-ibn) | ImageNet | 80.8% | 60.2% | 16.5% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50.yml) | ImageNet | 76.7% | 53.6% | 12.2% | -|
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| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50-ibn.yml) | ImageNet | 79.3% | 57.5% | 14.3% | -|
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| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_S50.yml) | ImageNet | 77.3% | 54.7% | 12.6% | -|
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| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R101-ibn.yml) | ImageNet | 80.8% | 60.2% | 16.5% | -|
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**SBS**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50) | ImageNet | 83.3% | 59.9% | 14.6% |
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| SBS(R50-ibn) | ImageNet | 84.0% | 61.2% | 15.5% |
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| SBS(S50) | ImageNet | 82.6% | 58.2% | 13.2% |
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| SBS(R101-ibn) | ImageNet | 85.1% | 63.3% | 16.6% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50.yml) | ImageNet | 83.3% | 59.9% | 14.6% | -|
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| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50-ibn.yml) | ImageNet | 84.0% | 61.2% | 15.5% | -|
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| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_S50.yml) | ImageNet | 82.6% | 58.2% | 13.2% | -|
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| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R101-ibn.yml) | ImageNet | 85.1% | 63.3% | 16.6% | -|
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**MGN**:
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| SBS(R50) | ImageNet | 82.9% | 61.2% | 14.9% |
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| SBS(R50-ibn) | ImageNet | 85.1% | 65.4% | 18.4% |
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| SBS(R101-ibn) | ImageNet | 96.3% | 90.3% | 70.0% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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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 |
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| :---: | :---: | :---: |:---: | :---: |
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| BoT(R50-ibn) | ImageNet | 97.0% | 81.9% | 46.3% |
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| Method | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| BoT(R50-ibn) | ImageNet | 97.0% | 81.9% | 46.3% | -|
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### VehicleID Baseline
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**BoT**:
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**BoT**:
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Method: BoT(R50-ibn+gem pooling+weighted triplet+soft margin)
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Test protocol: 10-fold cross-validation
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| Testset size | Pretrained | Rank@1 | Rank@5 |
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| :---: | :---: | :---: |:---: |
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| Small(800) | ImageNet | 86.6% | 97.9% |
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| Medium(1600) | ImageNet | 82.9% | 96.0% |
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| Large(2400) | ImageNet | 80.6% | 93.9% |
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| Testset size | Pretrained | Rank@1 | Rank@5 | download |
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| :---: | :---: | :---: |:---: | :---:|
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| Small(800) | ImageNet | 86.6% | 97.9% | -|
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| Medium(1600) | ImageNet | 82.9% | 96.0% | -|
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| Large(2400) | ImageNet | 80.6% | 93.9% | -|
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### VERI-Wild Baseline
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**BoT**:
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Method: BoT(R50-ibn+gem pooling+weighted triplet+soft margin)
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| Testset size | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| Small(3000) | ImageNet | 96.4% | 87.7% | 69.2% |
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| Medium(5000) | ImageNet | 95.1% | 83.5% | 61.2% |
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| Large(10000) | ImageNet | 92.5% | 77.3% | 49.8% |
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| Testset size | Pretrained | Rank@1 | mAP | mINP | download |
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| :---: | :---: | :---: |:---: | :---: | :---:|
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| Small(3000) | ImageNet | 96.4% | 87.7% | 69.2% | -|
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| Medium(5000) | ImageNet | 95.1% | 83.5% | 61.2% | -|
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| Large(10000) | ImageNet | 92.5% | 77.3% | 49.8% | -|
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Reference in New Issue