Skip to content

GitLab

"docs/en_US/Tuner/HyperbandAdvisor.rst" did not exist on "abc221589c65d75b494407c60a81ca87c3020463"
Switch branch/tag
Find file Normal viewHistoryPermalink
MODEL_ZOO.md 14.4 KB
Newer Older
dengjb's avatar
update  
dengjb committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247 
# FastReID Model Zoo and Baselines

## Introduction

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.

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

**BoT**:

[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.

**AGW**:

[ReID-Survey with a Powerful AGW Baseline](https://github.com/mangye16/ReID-Survey).

**MGN**:

[Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://arxiv.org/abs/1804.01438v1)

**SBS**:

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):

1. Non-local block
2. GeM pooling

### Market1501 Baselines

**BoT**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---: |
| [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) |

**AGW**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: |:---: |
| [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) |
| [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) |
| [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) |

**SBS**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: |:---:|
| [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) |
| [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) |
| [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) |

**MGN**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |

### DukeMTMC Baseline

**BoT**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---: |
| [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) |

**AGW**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |
| [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) |
| [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) |

**SBS**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |
| [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) |
| [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) |

**MGN**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |

### MSMT17 Baseline

**BoT**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |
| [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) |
| [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) |

**AGW**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |
| [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) |
| [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) |

**SBS**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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) |
| [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) |
| [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) |

**MGN**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:|
| [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% | - |

### VeRi Baseline

**SBS**:

| Method | Pretrained | Rank@1 | mAP | mINP | download |
| :---: | :---: | :---: |:---: | :---: | :---:| 
| [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) |

### VehicleID Baseline

**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>
    <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>
  </tr>
</tbody>
</table>

### VERI-Wild Baseline

**BoT**:  
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>
    <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>
  </tr>
</tbody>
</table>