Available pretrained models
Available pre-trained pytorch models are stored in bonseyes_openpifpaf_wholebody/models/pytorch
.
Models with shufflenetv2k16
and shufflenetv2k30
backbones were trained on COCO wholebody dataset (133 keypoints) and the rest of models were trained on COCO kp dataset (17 keypoints)
models
|-- pytorch
|-- mobilenetv2
| `-- v3.0_mobilenetv2_default_641x641_fp32.pkl
|-- mobilenetv3large
| `-- v3.0_mobilenetv3large_default_641x641_fp32.pkl
|-- mobilenetv3small
| `-- v3.0_mobilenetv3small_default_641x641_fp32.pkl
|-- resnet50
| `-- v3.0_resnet50_default_641x641_fp32.pkl
|-- shufflenetv2k16
| `-- v3.0_shufflenetv2k16_default_641x641_fp32.pkl
|-- shufflenetv2k30
| `-- v3.0_shufflenetv2k30_default_641x641_fp32.pkl
|-- swin-b
| `-- v3.0_swin-b_default_641x641_fp32.pkl
|-- swin-s
| `-- v3.0_swin-s_default_641x641_fp32.pkl
|-- swin-t
`-- v3.0_swin-t_default_641x641_fp32.pkl
Pretrained model summaries
For each backbone, these summaries contain detailed architecture info (csv tables available for download) and tables that hold estimates of:
Total number of network parameters
Theoretical amount of floating point arithmetics (FLOPs)
Theoretical amount of multiply-adds (MAdd)
Memory usage
ShuffleNet v2k30
Download information about layers for shufflenetv2k30 pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
45.3 |
3.6 |
97.3 |
7100 |
364.6 |
128x96 |
45.3 |
4.6 |
128.2 |
9100 |
426.1 |
128x128 |
45.3 |
6.1 |
170.9 |
12130 |
510.5 |
256x144 |
45.3 |
13.7 |
384.4 |
27290 |
932.7 |
256x192 |
45.3 |
18.2 |
512.6 |
36380 |
1187.8 |
256x256 |
45.3 |
24.3 |
683.5 |
48510 |
1525.8 |
384x216 |
45.3 |
31.2 |
868.6 |
62220 |
1884.2 |
384x288 |
45.3 |
41.0 |
1153.3 |
81860 |
2457.6 |
384x384 |
45.3 |
54.7 |
1537.8 |
109150 |
3215.4 |
512x288 |
45.3 |
54.7 |
1537.8 |
109150 |
3215.4 |
512x384 |
45.3 |
72.9 |
2050.3 |
145540 |
4229.1 |
512x512 |
45.3 |
97.2 |
2733.8 |
194050 |
5580.8 |
ShuffleNet v2k16
Download information about layers for shufflenetv2k16 pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
20.5 |
1.3 |
40.9 |
2570 |
157.0 |
128x96 |
20.5 |
1.6 |
53.5 |
3240 |
181.9 |
128x128 |
20.5 |
2.2 |
71.3 |
4330 |
216.4 |
256x144 |
20.5 |
4.9 |
160.6 |
9730 |
389.1 |
256x192 |
20.5 |
6.5 |
214.1 |
12980 |
492.7 |
256x256 |
20.5 |
8.7 |
285.4 |
17310 |
630.8 |
384x216 |
20.5 |
11.2 |
363.4 |
22330 |
780.6 |
384x288 |
20.5 |
14.6 |
481.6 |
29200 |
1010.7 |
384x384 |
20.5 |
19.5 |
642.2 |
38940 |
1321.0 |
512x288 |
20.5 |
19.5 |
642.2 |
38940 |
1321.0 |
512x384 |
20.5 |
26.0 |
856.2 |
51920 |
1740.8 |
512x512 |
20.5 |
34.7 |
1141.7 |
69220 |
2283.5 |
ResNet50
Download information about layers for resnet50 pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
25.5 |
3.1 |
76.3 |
6170 |
248.7 |
128x96 |
25.5 |
4.0 |
101.0 |
8060 |
298.0 |
128x128 |
25.5 |
5.4 |
134.7 |
10740 |
365.0 |
256x144 |
25.5 |
12.1 |
303.1 |
24170 |
699.9 |
256x192 |
25.5 |
16.1 |
404.1 |
32230 |
900.8 |
256x256 |
25.5 |
21.5 |
538.9 |
42970 |
1167.4 |
384x216 |
25.5 |
27.4 |
683.5 |
54780 |
1454.1 |
384x288 |
25.5 |
36.3 |
909.3 |
72510 |
1904.6 |
384x384 |
25.5 |
48.4 |
1212.4 |
96680 |
2508.8 |
512x288 |
25.5 |
48.4 |
1212.4 |
96680 |
2508.8 |
512x384 |
25.5 |
64.6 |
1616.5 |
128910 |
3307.5 |
512x512 |
25.5 |
86.1 |
2155.4 |
171880 |
4382.7 |
MobileNet v2
Download information about layers for mobilenetv2 pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
12.1 |
0.2 |
15.1 |
365.3 |
75.1 |
128x96 |
12.1 |
0.2 |
19.1 |
389.5 |
83.1 |
128x128 |
12.1 |
0.3 |
25.4 |
519.4 |
95.4 |
256x144 |
12.1 |
0.6 |
57.9 |
1260.0 |
157.9 |
256x192 |
12.1 |
0.8 |
76.2 |
1560.0 |
194.0 |
256x256 |
12.1 |
1.1 |
101.7 |
2080.0 |
243.3 |
384x216 |
12.1 |
1.4 |
130.1 |
2710.0 |
298.1 |
384x288 |
12.1 |
1.8 |
171.6 |
3510.0 |
378.9 |
384x384 |
12.1 |
2.4 |
228.7 |
4670.0 |
489.8 |
512x288 |
12.1 |
2.4 |
228.7 |
4670.0 |
489.8 |
512x384 |
12.1 |
3.1 |
305.0 |
6230.0 |
637.7 |
512x512 |
12.1 |
4.2 |
406.7 |
8310.0 |
834.9 |
MobileNet v3 small
Download information about layers for mobilenetv3small pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
1.5 |
0.1 |
12.5 |
130.8 |
24.7 |
128x96 |
1.5 |
0.1 |
16.4 |
164.6 |
30.7 |
128x128 |
1.5 |
0.1 |
21.9 |
219.2 |
39.0 |
256x144 |
1.5 |
0.2 |
49.2 |
492.0 |
80.8 |
256x192 |
1.5 |
0.3 |
65.6 |
655.7 |
105.9 |
256x256 |
1.5 |
0.4 |
87.5 |
873.9 |
139.3 |
384x216 |
1.5 |
0.6 |
111.3 |
1130.0 |
175.5 |
384x288 |
1.5 |
0.7 |
147.6 |
1470.0 |
231.1 |
384x384 |
1.5 |
1.0 |
196.8 |
1970.0 |
306.3 |
512x288 |
1.5 |
1.0 |
196.8 |
1970.0 |
306.3 |
512x384 |
1.5 |
1.3 |
262.4 |
2620.0 |
406.5 |
512x512 |
1.5 |
1.8 |
349.8 |
3490.0 |
540.1 |
MobileNet v3 large
Download information about layers for mobilenetv3large pytorch model of input size 384x216 csv
INPUT_SIZE |
#PARAMS |
GFLOPs |
memory |
MAdd |
MemR+W |
---|---|---|---|---|---|
128x72 |
3.9 |
0.2 |
37.9 |
407.2 |
77.9 |
128x96 |
3.9 |
0.3 |
50.0 |
522.0 |
98.4 |
128x128 |
3.9 |
0.4 |
66.7 |
695.1 |
126.3 |
256x144 |
3.9 |
0.8 |
150.1 |
1560.0 |
265.5 |
256x192 |
3.9 |
1.1 |
200.1 |
2080.0 |
349.1 |
256x256 |
3.9 |
1.4 |
266.8 |
2770.0 |
460.5 |
384x216 |
3.9 |
1.8 |
338.7 |
3550.0 |
580.0 |
384x288 |
3.9 |
2.4 |
450.2 |
4670.0 |
766.8 |
384x384 |
3.9 |
3.2 |
600.3 |
6230.0 |
1017.5 |
512x288 |
3.9 |
3.2 |
600.3 |
6230.0 |
1017.5 |
512x384 |
3.9 |
4.2 |
800.4 |
8310.0 |
1351.7 |
512x512 |
3.9 |
5.6 |
1067.2 |
11080.0 |
1802.2 |
When exporting tensorrt fp16 shufflenetv2k30 model (input size 320x320) with enabled DLA usage and GPU fallback, we observed the following distribution of layer execution:
Layers running on DLA: |
Layers running on GPU: |
---|---|
{Conv_0, Relu_1, Conv_2, Conv_5, Conv_3, Relu_6, Relu_4}, {Conv_8, Relu_9}, |
(Unnamed Layer* 1479) [Constant], (Unnamed Layer* 1675) [Constant], (Unnamed Layer* 1677) [Constant], Conv_7, 600 copy, 608 copy, Reshape_32 + Transpose_33, Reshape_38, Split_39, Split_39_1, Conv_40 + Relu_41, Conv_42, Conv_43 + Relu_44, Reshape_67 + Transpose_68, Reshape_73, Split_74, Split_74_1, Conv_75 + Relu_76, Conv_77, Conv_78 + Relu_79, Reshape_102 + Transpose_103, Reshape_108, Split_109, Split_109_1, Conv_110 + Relu_111, Conv_112, Conv_113 + Relu_114, Reshape_137 + Transpose_138, Reshape_143, Split_144, Split_144_1, Conv_145 + Relu_146, Conv_147, Conv_148 + Relu_149, Reshape_172 + Transpose_173, Reshape_178, Split_179, Split_179_1, Conv_180 + Relu_181, Conv_182, Conv_183 + Relu_184, Reshape_207 + Transpose_208, Reshape_213, Split_214, Split_214_1, Conv_215 + Relu_216, Conv_217, Conv_218 + Relu_219, Reshape_242 + Transpose_243, Reshape_248, Split_249, Split_249_1, Conv_250 + Relu_251, Conv_252, Conv_253 + Relu_254, Reshape_277 + Transpose_278, Reshape_283, Conv_284, Conv_287 + Relu_288, Conv_285 + Relu_286, Conv_289, Conv_290 + Relu_291, Reshape_314 + Transpose_315, Reshape_320, Split_321, Split_321_1, Conv_322 + Relu_323, Conv_324, Conv_325 + Relu_326, Reshape_349 + Transpose_350, Reshape_355, Split_356, Split_356_1, Conv_357 + Relu_358, Conv_359, Conv_360 + Relu_361, Reshape_384 + Transpose_385, Reshape_390, Split_391, Split_391_1, Conv_392 + Relu_393, Conv_394, Conv_395 + Relu_396, Reshape_419 + Transpose_420, Reshape_425, Split_426, Split_426_1, Conv_427 + Relu_428, Conv_429, Conv_430 + Relu_431, Reshape_454 + Transpose_455, Reshape_460, Split_461, Split_461_1, Conv_462 + Relu_463, Conv_464, Conv_465 + Relu_466, Reshape_489 + Transpose_490, Reshape_495, Split_496, Split_496_1, Conv_497 + Relu_498, Conv_499, Conv_500 + Relu_501, Reshape_524 + Transpose_525, Reshape_530, Split_531, Split_531_1, Conv_532 + Relu_533, Conv_534, Conv_535 + Relu_536, Reshape_559 + Transpose_560, Reshape_565, Split_566, Split_566_1, Conv_567 + Relu_568, Conv_569, Conv_570 + Relu_571, Reshape_594 + Transpose_595, Reshape_600, Split_601, Split_601_1, Conv_602 + Relu_603, Conv_604, Conv_605 + Relu_606, Reshape_629 + Transpose_630, Reshape_635, Split_636, Split_636_1, Conv_637 + Relu_638, Conv_639, Conv_640 + Relu_641, Reshape_664 + Transpose_665, Reshape_670, Split_671, Split_671_1, Conv_672 + Relu_673, Conv_674, Conv_675 + Relu_676, Reshape_699 + Transpose_700, Reshape_705, Split_706, Split_706_1, Conv_707 + Relu_708, Conv_709, Conv_710 + Relu_711, Reshape_734 + Transpose_735, Reshape_740, Split_741, Split_741_1, Conv_742 + Relu_743, Conv_744, Conv_745 + Relu_746, Reshape_769 + Transpose_770, Reshape_775, Split_776, Split_776_1, Conv_777 + Relu_778, Conv_779, Conv_780 + Relu_781, Reshape_804 + Transpose_805, Reshape_810, Split_811, Split_811_1, Conv_812 + Relu_813, Conv_814, Conv_815 + Relu_816, Reshape_839 + Transpose_840, Reshape_845, Conv_846, Conv_849 + Relu_850, Conv_847 + Relu_848, Conv_851, Conv_852 + Relu_853, Reshape_876 + Transpose_877, Reshape_882, Split_883, Split_883_1, Conv_884 + Relu_885, Conv_886, Conv_887 + Relu_888, Reshape_911 + Transpose_912, Reshape_917, Split_918, Split_918_1, Conv_919 + Relu_920, Conv_921, Conv_922 + Relu_923, Reshape_946 + Transpose_947, Reshape_952, Split_953, Split_953_1, Conv_954 + Relu_955, Conv_956, Conv_957 + Relu_958, Reshape_981 + Transpose_982, Reshape_987, Split_988, Split_988_1, Conv_989 + Relu_990, Conv_991, Conv_992 + Relu_993, Reshape_1016 + Transpose_1017, Reshape_1022, Split_1023, Split_1023_1, Conv_1024 + Relu_1025, Conv_1026, Conv_1027 + Relu_1028, Reshape_1051 + Transpose_1052, Reshape_1057, Conv_1058 + Relu_1059, Conv_1085 || Conv_1060, Reshape_1062 + Transpose_1063, Reshape_1087 + Transpose_1088, Reshape_1065, Reshape_1090, Slice_1066, Slice_1091, Slice_1067, Slice_1092, Reshape_1073 + Transpose_1074, Reshape_1098 + Transpose_1099, Slice_1075, Slice_1077, Slice_1080, Slice_1081, Slice_1100, Slice_1102, Slice_1103, Slice_1108, Slice_1109, Sigmoid_1076, Add_1079, Softplus_1082, Sigmoid_1101, Add_1105, Add_1107, 1922 copy, 1925 copy, 1926 copy, 1928 copy, Transpose_1084, Softplus_1110, 1955 copy, 1959 copy, 1961 copy, 1962 copy, 1964 copy, Transpose_1112, |