|
a |
|
b/configs/gcnet/gcnet.yml |
|
|
1 |
Collections: |
|
|
2 |
- Name: gcnet |
|
|
3 |
Metadata: |
|
|
4 |
Training Data: |
|
|
5 |
- Cityscapes |
|
|
6 |
- ADE20K |
|
|
7 |
- Pascal VOC 2012 + Aug |
|
|
8 |
Paper: |
|
|
9 |
URL: https://arxiv.org/abs/1904.11492 |
|
|
10 |
Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' |
|
|
11 |
README: configs/gcnet/README.md |
|
|
12 |
Code: |
|
|
13 |
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10 |
|
|
14 |
Version: v0.17.0 |
|
|
15 |
Converted From: |
|
|
16 |
Code: https://github.com/xvjiarui/GCNet |
|
|
17 |
Models: |
|
|
18 |
- Name: gcnet_r50-d8_512x1024_40k_cityscapes |
|
|
19 |
In Collection: gcnet |
|
|
20 |
Metadata: |
|
|
21 |
backbone: R-50-D8 |
|
|
22 |
crop size: (512,1024) |
|
|
23 |
lr schd: 40000 |
|
|
24 |
inference time (ms/im): |
|
|
25 |
- value: 254.45 |
|
|
26 |
hardware: V100 |
|
|
27 |
backend: PyTorch |
|
|
28 |
batch size: 1 |
|
|
29 |
mode: FP32 |
|
|
30 |
resolution: (512,1024) |
|
|
31 |
Training Memory (GB): 5.8 |
|
|
32 |
Results: |
|
|
33 |
- Task: Semantic Segmentation |
|
|
34 |
Dataset: Cityscapes |
|
|
35 |
Metrics: |
|
|
36 |
mIoU: 77.69 |
|
|
37 |
mIoU(ms+flip): 78.56 |
|
|
38 |
Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py |
|
|
39 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth |
|
|
40 |
- Name: gcnet_r101-d8_512x1024_40k_cityscapes |
|
|
41 |
In Collection: gcnet |
|
|
42 |
Metadata: |
|
|
43 |
backbone: R-101-D8 |
|
|
44 |
crop size: (512,1024) |
|
|
45 |
lr schd: 40000 |
|
|
46 |
inference time (ms/im): |
|
|
47 |
- value: 383.14 |
|
|
48 |
hardware: V100 |
|
|
49 |
backend: PyTorch |
|
|
50 |
batch size: 1 |
|
|
51 |
mode: FP32 |
|
|
52 |
resolution: (512,1024) |
|
|
53 |
Training Memory (GB): 9.2 |
|
|
54 |
Results: |
|
|
55 |
- Task: Semantic Segmentation |
|
|
56 |
Dataset: Cityscapes |
|
|
57 |
Metrics: |
|
|
58 |
mIoU: 78.28 |
|
|
59 |
mIoU(ms+flip): 79.34 |
|
|
60 |
Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py |
|
|
61 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth |
|
|
62 |
- Name: gcnet_r50-d8_769x769_40k_cityscapes |
|
|
63 |
In Collection: gcnet |
|
|
64 |
Metadata: |
|
|
65 |
backbone: R-50-D8 |
|
|
66 |
crop size: (769,769) |
|
|
67 |
lr schd: 40000 |
|
|
68 |
inference time (ms/im): |
|
|
69 |
- value: 598.8 |
|
|
70 |
hardware: V100 |
|
|
71 |
backend: PyTorch |
|
|
72 |
batch size: 1 |
|
|
73 |
mode: FP32 |
|
|
74 |
resolution: (769,769) |
|
|
75 |
Training Memory (GB): 6.5 |
|
|
76 |
Results: |
|
|
77 |
- Task: Semantic Segmentation |
|
|
78 |
Dataset: Cityscapes |
|
|
79 |
Metrics: |
|
|
80 |
mIoU: 78.12 |
|
|
81 |
mIoU(ms+flip): 80.09 |
|
|
82 |
Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py |
|
|
83 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth |
|
|
84 |
- Name: gcnet_r101-d8_769x769_40k_cityscapes |
|
|
85 |
In Collection: gcnet |
|
|
86 |
Metadata: |
|
|
87 |
backbone: R-101-D8 |
|
|
88 |
crop size: (769,769) |
|
|
89 |
lr schd: 40000 |
|
|
90 |
inference time (ms/im): |
|
|
91 |
- value: 884.96 |
|
|
92 |
hardware: V100 |
|
|
93 |
backend: PyTorch |
|
|
94 |
batch size: 1 |
|
|
95 |
mode: FP32 |
|
|
96 |
resolution: (769,769) |
|
|
97 |
Training Memory (GB): 10.5 |
|
|
98 |
Results: |
|
|
99 |
- Task: Semantic Segmentation |
|
|
100 |
Dataset: Cityscapes |
|
|
101 |
Metrics: |
|
|
102 |
mIoU: 78.95 |
|
|
103 |
mIoU(ms+flip): 80.71 |
|
|
104 |
Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py |
|
|
105 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth |
|
|
106 |
- Name: gcnet_r50-d8_512x1024_80k_cityscapes |
|
|
107 |
In Collection: gcnet |
|
|
108 |
Metadata: |
|
|
109 |
backbone: R-50-D8 |
|
|
110 |
crop size: (512,1024) |
|
|
111 |
lr schd: 80000 |
|
|
112 |
Results: |
|
|
113 |
- Task: Semantic Segmentation |
|
|
114 |
Dataset: Cityscapes |
|
|
115 |
Metrics: |
|
|
116 |
mIoU: 78.48 |
|
|
117 |
mIoU(ms+flip): 80.01 |
|
|
118 |
Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py |
|
|
119 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth |
|
|
120 |
- Name: gcnet_r101-d8_512x1024_80k_cityscapes |
|
|
121 |
In Collection: gcnet |
|
|
122 |
Metadata: |
|
|
123 |
backbone: R-101-D8 |
|
|
124 |
crop size: (512,1024) |
|
|
125 |
lr schd: 80000 |
|
|
126 |
Results: |
|
|
127 |
- Task: Semantic Segmentation |
|
|
128 |
Dataset: Cityscapes |
|
|
129 |
Metrics: |
|
|
130 |
mIoU: 79.03 |
|
|
131 |
mIoU(ms+flip): 79.84 |
|
|
132 |
Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py |
|
|
133 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth |
|
|
134 |
- Name: gcnet_r50-d8_769x769_80k_cityscapes |
|
|
135 |
In Collection: gcnet |
|
|
136 |
Metadata: |
|
|
137 |
backbone: R-50-D8 |
|
|
138 |
crop size: (769,769) |
|
|
139 |
lr schd: 80000 |
|
|
140 |
Results: |
|
|
141 |
- Task: Semantic Segmentation |
|
|
142 |
Dataset: Cityscapes |
|
|
143 |
Metrics: |
|
|
144 |
mIoU: 78.68 |
|
|
145 |
mIoU(ms+flip): 80.66 |
|
|
146 |
Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py |
|
|
147 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth |
|
|
148 |
- Name: gcnet_r101-d8_769x769_80k_cityscapes |
|
|
149 |
In Collection: gcnet |
|
|
150 |
Metadata: |
|
|
151 |
backbone: R-101-D8 |
|
|
152 |
crop size: (769,769) |
|
|
153 |
lr schd: 80000 |
|
|
154 |
Results: |
|
|
155 |
- Task: Semantic Segmentation |
|
|
156 |
Dataset: Cityscapes |
|
|
157 |
Metrics: |
|
|
158 |
mIoU: 79.18 |
|
|
159 |
mIoU(ms+flip): 80.71 |
|
|
160 |
Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py |
|
|
161 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth |
|
|
162 |
- Name: gcnet_r50-d8_512x512_80k_ade20k |
|
|
163 |
In Collection: gcnet |
|
|
164 |
Metadata: |
|
|
165 |
backbone: R-50-D8 |
|
|
166 |
crop size: (512,512) |
|
|
167 |
lr schd: 80000 |
|
|
168 |
inference time (ms/im): |
|
|
169 |
- value: 42.77 |
|
|
170 |
hardware: V100 |
|
|
171 |
backend: PyTorch |
|
|
172 |
batch size: 1 |
|
|
173 |
mode: FP32 |
|
|
174 |
resolution: (512,512) |
|
|
175 |
Training Memory (GB): 8.5 |
|
|
176 |
Results: |
|
|
177 |
- Task: Semantic Segmentation |
|
|
178 |
Dataset: ADE20K |
|
|
179 |
Metrics: |
|
|
180 |
mIoU: 41.47 |
|
|
181 |
mIoU(ms+flip): 42.85 |
|
|
182 |
Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py |
|
|
183 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth |
|
|
184 |
- Name: gcnet_r101-d8_512x512_80k_ade20k |
|
|
185 |
In Collection: gcnet |
|
|
186 |
Metadata: |
|
|
187 |
backbone: R-101-D8 |
|
|
188 |
crop size: (512,512) |
|
|
189 |
lr schd: 80000 |
|
|
190 |
inference time (ms/im): |
|
|
191 |
- value: 65.79 |
|
|
192 |
hardware: V100 |
|
|
193 |
backend: PyTorch |
|
|
194 |
batch size: 1 |
|
|
195 |
mode: FP32 |
|
|
196 |
resolution: (512,512) |
|
|
197 |
Training Memory (GB): 12.0 |
|
|
198 |
Results: |
|
|
199 |
- Task: Semantic Segmentation |
|
|
200 |
Dataset: ADE20K |
|
|
201 |
Metrics: |
|
|
202 |
mIoU: 42.82 |
|
|
203 |
mIoU(ms+flip): 44.54 |
|
|
204 |
Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py |
|
|
205 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth |
|
|
206 |
- Name: gcnet_r50-d8_512x512_160k_ade20k |
|
|
207 |
In Collection: gcnet |
|
|
208 |
Metadata: |
|
|
209 |
backbone: R-50-D8 |
|
|
210 |
crop size: (512,512) |
|
|
211 |
lr schd: 160000 |
|
|
212 |
Results: |
|
|
213 |
- Task: Semantic Segmentation |
|
|
214 |
Dataset: ADE20K |
|
|
215 |
Metrics: |
|
|
216 |
mIoU: 42.37 |
|
|
217 |
mIoU(ms+flip): 43.52 |
|
|
218 |
Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py |
|
|
219 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth |
|
|
220 |
- Name: gcnet_r101-d8_512x512_160k_ade20k |
|
|
221 |
In Collection: gcnet |
|
|
222 |
Metadata: |
|
|
223 |
backbone: R-101-D8 |
|
|
224 |
crop size: (512,512) |
|
|
225 |
lr schd: 160000 |
|
|
226 |
Results: |
|
|
227 |
- Task: Semantic Segmentation |
|
|
228 |
Dataset: ADE20K |
|
|
229 |
Metrics: |
|
|
230 |
mIoU: 43.69 |
|
|
231 |
mIoU(ms+flip): 45.21 |
|
|
232 |
Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py |
|
|
233 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth |
|
|
234 |
- Name: gcnet_r50-d8_512x512_20k_voc12aug |
|
|
235 |
In Collection: gcnet |
|
|
236 |
Metadata: |
|
|
237 |
backbone: R-50-D8 |
|
|
238 |
crop size: (512,512) |
|
|
239 |
lr schd: 20000 |
|
|
240 |
inference time (ms/im): |
|
|
241 |
- value: 42.83 |
|
|
242 |
hardware: V100 |
|
|
243 |
backend: PyTorch |
|
|
244 |
batch size: 1 |
|
|
245 |
mode: FP32 |
|
|
246 |
resolution: (512,512) |
|
|
247 |
Training Memory (GB): 5.8 |
|
|
248 |
Results: |
|
|
249 |
- Task: Semantic Segmentation |
|
|
250 |
Dataset: Pascal VOC 2012 + Aug |
|
|
251 |
Metrics: |
|
|
252 |
mIoU: 76.42 |
|
|
253 |
mIoU(ms+flip): 77.51 |
|
|
254 |
Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py |
|
|
255 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth |
|
|
256 |
- Name: gcnet_r101-d8_512x512_20k_voc12aug |
|
|
257 |
In Collection: gcnet |
|
|
258 |
Metadata: |
|
|
259 |
backbone: R-101-D8 |
|
|
260 |
crop size: (512,512) |
|
|
261 |
lr schd: 20000 |
|
|
262 |
inference time (ms/im): |
|
|
263 |
- value: 67.57 |
|
|
264 |
hardware: V100 |
|
|
265 |
backend: PyTorch |
|
|
266 |
batch size: 1 |
|
|
267 |
mode: FP32 |
|
|
268 |
resolution: (512,512) |
|
|
269 |
Training Memory (GB): 9.2 |
|
|
270 |
Results: |
|
|
271 |
- Task: Semantic Segmentation |
|
|
272 |
Dataset: Pascal VOC 2012 + Aug |
|
|
273 |
Metrics: |
|
|
274 |
mIoU: 77.41 |
|
|
275 |
mIoU(ms+flip): 78.56 |
|
|
276 |
Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py |
|
|
277 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth |
|
|
278 |
- Name: gcnet_r50-d8_512x512_40k_voc12aug |
|
|
279 |
In Collection: gcnet |
|
|
280 |
Metadata: |
|
|
281 |
backbone: R-50-D8 |
|
|
282 |
crop size: (512,512) |
|
|
283 |
lr schd: 40000 |
|
|
284 |
Results: |
|
|
285 |
- Task: Semantic Segmentation |
|
|
286 |
Dataset: Pascal VOC 2012 + Aug |
|
|
287 |
Metrics: |
|
|
288 |
mIoU: 76.24 |
|
|
289 |
mIoU(ms+flip): 77.63 |
|
|
290 |
Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py |
|
|
291 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth |
|
|
292 |
- Name: gcnet_r101-d8_512x512_40k_voc12aug |
|
|
293 |
In Collection: gcnet |
|
|
294 |
Metadata: |
|
|
295 |
backbone: R-101-D8 |
|
|
296 |
crop size: (512,512) |
|
|
297 |
lr schd: 40000 |
|
|
298 |
Results: |
|
|
299 |
- Task: Semantic Segmentation |
|
|
300 |
Dataset: Pascal VOC 2012 + Aug |
|
|
301 |
Metrics: |
|
|
302 |
mIoU: 77.84 |
|
|
303 |
mIoU(ms+flip): 78.59 |
|
|
304 |
Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py |
|
|
305 |
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth |