Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
MODEL_ZOO.md 13.9 KB
Newer Older
limm's avatar
push v0.1.3 version commit bd2ea47  
limm 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
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277 
# Model Zoo and Baselines

# Introduction

We provide baselines trained with Detectron2 DensePose. The corresponding
configuration files can be found in the [configs](../configs) directory.
All models were trained on COCO `train2014` + `valminusminival2014` and
evaluated on COCO `minival2014`. For the details on common settings in which
baselines were trained, please check [Detectron 2 Model Zoo](../../../MODEL_ZOO.md).

## License

All models available for download through this document are licensed under the
[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/)

## COCO DensePose Baselines with DensePose-RCNN

### Legacy Models

Baselines trained using schedules from [Güler et al, 2018](https://arxiv.org/pdf/1802.00434.pdf)

<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">dp. AP<br/>GPS</th>
<th valign="bottom">dp. AP<br/>GPSm</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: densepose_rcnn_R_50_FPN_s1x_legacy -->
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml">R_50_FPN_s1x_legacy</a></td>
 <td align="center">s1x</td>
 <td align="center">0.307</td>
 <td align="center">0.051</td>
 <td align="center">3.2</td>
 <td align="center">58.1</td>
 <td align="center">52.1</td>
 <td align="center">54.9</td>
 <td align="center">164832157</td>
 <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/model_final_d366fa.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/metrics.json">metrics</a></td>
 </tr>
 <!-- ROW: densepose_rcnn_R_101_FPN_s1x_legacy -->
  <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml">R_101_FPN_s1x_legacy</a></td>
  <td align="center">s1x</td>
  <td align="center">0.390</td>
  <td align="center">0.063</td>
  <td align="center">4.3</td>
  <td align="center">59.5</td>
  <td align="center">53.2</td>
  <td align="center">56.1</td>
  <td align="center">164832182</td>
  <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/model_final_10af0e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/metrics.json">metrics</a></td>
  </tr>
</tbody></table>

### Improved Baselines, Original Fully Convolutional Haad

These models use an improved training schedule and Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446).

<table><tbody>
  <!-- START TABLE -->
  <!-- TABLE HEADER -->
  <th valign="bottom">Name</th>
  <th valign="bottom">lr<br/>sched</th>
  <th valign="bottom">train<br/>time<br/>(s/iter)</th>
  <th valign="bottom">inference<br/>time<br/>(s/im)</th>
  <th valign="bottom">train<br/>mem<br/>(GB)</th>
  <th valign="bottom">box<br/>AP</th>
  <th valign="bottom">dp. AP<br/>GPS</th>
  <th valign="bottom">dp. AP<br/>GPSm</th>
  <th valign="bottom">model id</th>
  <th valign="bottom">download</th>
  <!-- TABLE BODY -->
  <!-- ROW: densepose_rcnn_R_50_FPN_s1x -->
   <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_s1x.yaml">R_50_FPN_s1x</a></td>
   <td align="center">s1x</td>
   <td align="center">0.359</td>
   <td align="center">0.066</td>
   <td align="center">4.5</td>
   <td align="center">61.2</td>
   <td align="center">63.7</td>
   <td align="center">65.3</td>
   <td align="center">165712039</td>
   <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/metrics.json">metrics</a></td>
   </tr>
   <!-- ROW: densepose_rcnn_R_101_FPN_s1x -->
    <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_s1x.yaml">R_101_FPN_s1x</a></td>
    <td align="center">s1x</td>
    <td align="center">0.428</td>
    <td align="center">0.079</td>
    <td align="center">5.8</td>
    <td align="center">62.3</td>
    <td align="center">64.5</td>
    <td align="center">66.4</td>
    <td align="center">165712084</td>
    <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/model_final_c6ab63.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/metrics.json">metrics</a></td>
    </tr>
    </tbody></table>

### Improved Baselines, DeepLabV3 Head

These models use an improved training schedule, Panoptic FPN head from [Kirillov et al, 2019](https://arxiv.org/abs/1901.02446) and DeepLabV3 head from [Chen et al, 2017](https://arxiv.org/abs/1706.05587).

<table><tbody>
    <!-- START TABLE -->
    <!-- TABLE HEADER -->
    <th valign="bottom">Name</th>
    <th valign="bottom">lr<br/>sched</th>
    <th valign="bottom">train<br/>time<br/>(s/iter)</th>
    <th valign="bottom">inference<br/>time<br/>(s/im)</th>
    <th valign="bottom">train<br/>mem<br/>(GB)</th>
    <th valign="bottom">box<br/>AP</th>
    <th valign="bottom">dp. AP<br/>GPS</th>
    <th valign="bottom">dp. AP<br/>GPSm</th>
    <th valign="bottom">model id</th>
    <th valign="bottom">download</th>
    <!-- TABLE BODY -->
    <!-- ROW: densepose_rcnn_R_50_FPN_DL_s1x -->
     <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml">R_50_FPN_DL_s1x</a></td>
     <td align="center">s1x</td>
     <td align="center">0.392</td>
     <td align="center">0.070</td>
     <td align="center">6.7</td>
     <td align="center">61.1</td>
     <td align="center">65.6</td>
     <td align="center">66.8</td>
     <td align="center">165712097</td>
     <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/model_final_0ed407.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/metrics.json">metrics</a></td>
     </tr>
     <!-- ROW: densepose_rcnn_R_101_FPN_DL_s1x -->
      <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml">R_101_FPN_DL_s1x</a></td>
      <td align="center">s1x</td>
      <td align="center">0.478</td>
      <td align="center">0.083</td>
      <td align="center">7.0</td>
      <td align="center">62.3</td>
      <td align="center">66.3</td>
      <td align="center">67.7</td>
      <td align="center">165712116</td>
      <td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/model_final_844d15.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/metrics.json">metrics</a></td>
      </tr>
</tbody></table>

### Baselines with Confidence Estimation

These models perform additional estimation of confidence in regressed UV coodrinates, along the lines of [Neverova et al., 2019](https://papers.nips.cc/paper/8378-correlated-uncertainty-for-learning-dense-correspondences-from-noisy-labels).

<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">dp. AP<br/>GPS</th>
<th valign="bottom">dp. AP<br/>GPSm</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY --> 
<!-- ROW: densepose_rcnn_R_50_FPN_WC1_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC1_s1x.yaml">R_50_FPN_WC1_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.353</td>
<td align="center">0.064</td>
<td align="center">4.6</td>
<td align="center">60.5</td>
<td align="center">64.2</td>
<td align="center">65.6</td>
<td align="center">173862049</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/model_final_289019.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1_s1x/173862049/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_50_FPN_WC2_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_WC2_s1x.yaml">R_50_FPN_WC2_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.364</td>
<td align="center">0.066</td>
<td align="center">4.8</td>
<td align="center">60.7</td>
<td align="center">64.2</td>
<td align="center">65.7</td>
<td align="center">173861455</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/model_final_3abe14.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC2_s1x/173861455/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC1_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml">R_50_FPN_DL_WC1_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.397</td>
<td align="center">0.068</td>
<td align="center">6.7</td>
<td align="center">61.1</td>
<td align="center">65.8</td>
<td align="center">67.1</td>
<td align="center">173067973</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/model_final_b1e525.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC1_s1x/173067973/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_50_FPN_DL_WC2_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml">R_50_FPN_DL_WC2_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.410</td>
<td align="center">0.070</td>
<td align="center">6.8</td>
<td align="center">60.8</td>
<td align="center">65.6</td>
<td align="center">66.7</td>
<td align="center">173859335</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/model_final_60fed4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_WC2_s1x/173859335/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_101_FPN_WC1_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml">R_101_FPN_WC1_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.435</td>
<td align="center">0.076</td>
<td align="center">5.7</td>
<td align="center">62.5</td>
<td align="center">64.9</td>
<td align="center">66.5</td>
<td align="center">171402969</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/model_final_9e47f0.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC1_s1x/171402969/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_101_FPN_WC2_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml">R_101_FPN_WC2_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.450</td>
<td align="center">0.078</td>
<td align="center">5.7</td>
<td align="center">62.3</td>
<td align="center">64.8</td>
<td align="center">66.6</td>
<td align="center">173860702</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/model_final_5ea023.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_WC2_s1x/173860702/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC1_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml">R_101_FPN_DL_WC1_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.479</td>
<td align="center">0.081</td>
<td align="center">7.9</td>
<td align="center">62.0</td>
<td align="center">66.2</td>
<td align="center">67.4</td>
<td align="center">173858525</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/model_final_f359f3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC1_s1x/173858525/metrics.json">metrics</a></td>
</tr>
<!-- ROW: densepose_rcnn_R_101_FPN_DL_WC2_s1x --> 
 <tr><td align="left"><a href="../configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml">R_101_FPN_DL_WC2_s1x</a></td>
<td align="center">s1x</td>
<td align="center">0.491</td>
<td align="center">0.082</td>
<td align="center">7.6</td>
<td align="center">61.7</td>
<td align="center">65.9</td>
<td align="center">67.3</td>
<td align="center">173294801</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/model_final_6e1ed1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_WC2_s1x/173294801/metrics.json">metrics</a></td>
</tr>
</tbody></table>

## Old Baselines

It is still possible to use some baselines from [DensePose 1](https://github.com/facebookresearch/DensePose).
Below are evaluation metrics for the baselines recomputed in the current framework:

| Model | bbox AP | AP  |  AP50 | AP75  | APm  |APl |
|-----|-----|-----|---    |---    |---   |--- |
| [`ResNet50_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl) | 54.673 | 48.894 | 84.963 | 50.717 | 43.132 | 50.433 |
| [`ResNet101_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl) | 56.032 | 51.088 | 86.250 | 55.057 | 46.542 | 52.563 |

Note: these scores are close, but not strictly equal to the ones reported in the [DensePose 1 Model Zoo](https://github.com/facebookresearch/DensePose/blob/master/MODEL_ZOO.md),
which is due to small incompatibilities between the frameworks.