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model
string
input_dimensions
string
input_dtypes
string
output_dimensions
string
output_dtypes
string
conv_flops
int64
matmul_flops
int64
elementwise_mb
float64
reduction_mb
float64
normalization_mb
float64
movement_mb
float64
Abs
int64
Acos
int64
Add
int64
ai.onnx.ml::CategoryMapper
int64
And
int64
ArgMax
int64
AveragePool
int64
BatchNormalization
int64
Cast
int64
Ceil
int64
Clip
int64
com.microsoft::BiasGelu
int64
com.microsoft::DynamicQuantizeLSTM
int64
com.microsoft::DynamicQuantizeMatMul
int64
com.microsoft::FastGelu
int64
com.microsoft::FusedConv
int64
com.microsoft::FusedGemm
int64
com.microsoft::FusedMatMul
int64
com.microsoft::MatMulIntegerToFloat
int64
com.microsoft::QGemm
int64
com.microsoft::QLinearAdd
int64
com.microsoft::QLinearAveragePool
int64
com.microsoft::QLinearConcat
int64
com.microsoft::QLinearGlobalAveragePool
int64
com.microsoft::QLinearLeakyRelu
int64
com.microsoft::QLinearMul
int64
com.microsoft::QLinearSigmoid
int64
com.microsoft::QuickGelu
int64
com.microsoft::SkipLayerNormalization
int64
Compress
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int64
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int64
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int64
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int64
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int64
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int64
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int64
Div
int64
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int64
DynamicQuantizeLinear
int64
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int64
Equal
int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
Or
int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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float64
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float64
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float64
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float64
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int64
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string
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float64
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float64
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float64
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float64
hardcorenas_d_Opset17_extended.onnx
x:1x3x224x224
x:float32
668:1x1000
668:float32
472,440,512
2,560,000
50.528229
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
hardcorenas_d_Opset17.onnx
14.069188
0.082284
13.931521
14.247209
vit_base_patch8_224_in21k_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1089:1x21843
1089:float32
231,211,008
156,097,479,168
1,771.169711
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792.141724
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16,002
64
64
1,024
2,450
205
amd
epyc
gcloud
vit_base_patch8_224_in21k_Opset17.onnx
1,481.456045
150.199862
1,094.324125
1,632.506909
tf_efficientnetv2_m_in21ft1k_Opset17_basic.onnx
x:1x3x384x384
x:float32
2466:1x1000
2466:float32
31,470,991,360
2,560,000
1,201.339462
67.736023
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11
2
32,106
64
64
1,024
2,449.998
205
amd
epyc
gcloud
tf_efficientnetv2_m_in21ft1k_Opset17.onnx
301.127424
12.889699
286.464729
324.336829
shufflenet_v2_x1_5_Opset16.onnx
x:1x3x224x224
x:float32
1137:1x1000
1137:float32
266,034,528
2,048,000
8.246674
1.630875
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34.094547
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1
7,950
32
32
512
2,449.998
205
amd
epyc
gcloud
shufflenet_v2_x1_5_Opset16.onnx
8.161246
0.036559
8.08003
8.1978
wide_resnet101_2_Opset16_timm.onnx
x:1x3x224x224
x:float32
971:1x1000
971:float32
45,502,005,248
4,096,000
252.65625
4.21875
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
wide_resnet101_2_Opset16_timm.onnx
38.147223
0.068437
38.035275
38.292409
efficientnet_b2a_Opset17_basic.onnx
x:1x3x256x256
x:float32
918:1x1000
918:float32
1,716,891,520
2,816,000
265.528786
18.886337
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7,950
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1,024
2,449.998
205
amd
epyc
gcloud
efficientnet_b2a_Opset17.onnx
51.859786
2.52541
46.709466
54.158804
tf_efficientnet_lite1_Opset18_extended.onnx
x:1x3x240x240
x:float32
1124:1x1000
1124:float32
489,254,400
2,560,000
4.847168
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32,105
192
192
6,144
2,800.206
141
intel
xeon
gcloud
tf_efficientnet_lite1_Opset18.onnx
48.402357
0.2196
48.209532
48.906584
vit_srelpos_medium_patch16_224_Opset16_basic.onnx
x:1x3x224x224
x:float32
1373:1x1000
1373:float32
154,140,672
15,742,640,128
143.314545
0.384773
47.375
240.408928
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amd
epyc
gcloud
vit_srelpos_medium_patch16_224_Opset16.onnx
59.05226
2.849967
52.203256
63.263266
prophetnet_Opset18_basic.onnx
input_ids:1x128; attention_mask:1x128; decoder_input_ids:1x128
input_ids:int64; attention_mask:float32; decoder_input_ids:int64
7800:1x128x1024; 7809:1x256x1024; onnx::Transpose_2302:1x16x128x64; onnx::Einsum_2319:1x16x128x64; onnx::Reshape_2595:1x16x128x64; onnx::Reshape_2600:1x16x128x64; onnx::Transpose_2798:1x16x128x64; onnx::Einsum_2815:1x16x128x64; onnx::Reshape_3060:1x16x128x64; onnx::Reshape_3065:1x16x128x64; onnx::Transpose_3263:1x16x12...
7800:float32; 7809:float32; onnx::Transpose_2302:float32; onnx::Einsum_2319:float32; onnx::Reshape_2595:float32; onnx::Reshape_2600:float32; onnx::Transpose_2798:float32; onnx::Einsum_2815:float32; onnx::Reshape_3060:float32; onnx::Reshape_3065:float32; onnx::Transpose_3263:float32; onnx::Einsum_3280:float32; onnx::Res...
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amd
epyc
gcloud
prophetnet_Opset18.onnx
738.356021
21.123669
706.914221
771.900864
regnetx_120_Opset18_extended.onnx
x:1x3x224x224
x:float32
578:1x1000
578:float32
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4,480,000
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
regnetx_120_Opset18.onnx
60.155682
1.105886
58.451899
62.04915
vit_base_patch8_224_in21k_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1089:1x21843
1089:float32
231,211,008
156,097,479,168
1,771.169711
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792.141724
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
vit_base_patch8_224_in21k_Opset17.onnx
291.516247
1.238162
290.627471
294.793762
cs3sedarknet_x_Opset17.onnx
x:1x3x256x256
x:float32
929:1x1000
929:float32
16,708,436,480
2,560,000
276.64917
12.374939
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xeon_plat
bluehive
convnext_large_384_in22ft1k_Opset18.onnx
962.564802
2.050155
959.207623
966.316356
vit_relpos_base_patch16_224_Opset16_extended.onnx
x:1x3x224x224
x:float32
1593:1x1000
1593:float32
231,211,008
34,004,004,864
187.40918
0.577156
69.919922
360.612053
0
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49
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4
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
vit_relpos_base_patch16_224_Opset16.onnx
62.988332
0.071867
62.867205
63.071138
efficientnet_es_pruned_Opset18_extended.onnx
x:1x3x224x224
x:float32
440:1x1000
440:float32
672,759,808
2,560,000
4.378418
0.244141
0
0.009766
0
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2
16,002
64
64
1,024
2,450
205
amd
epyc
gcloud
efficientnet_es_pruned_Opset18.onnx
48.143793
1.568228
46.503874
51.054734
vit_small_patch32_224_Opset16_disable_all.onnx
x:1x3x224x224
x:float32
1339:1x1000
1339:float32
115,605,504
2,170,214,400
74.969508
3.672028
1.373291
26.521637
0
0
135
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37
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1
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
vit_small_patch32_224_Opset16.onnx
17.725544
0.067353
17.644105
17.834464
convit_small_Opset18_disable_all.onnx
x:1x3x224x224
x:float32
1400:1x1000
1400:float32
130,056,192
11,143,600,704
386.781783
13.256454
73.688736
132.315502
0
0
93
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57
0
10
11
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2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
convit_small_Opset18.onnx
56.894615
0.095911
56.715171
57.024921
tf_efficientnet_b3_ns_Opset17.onnx
x:1x3x300x300
x:float32
1502:1x1000
1502:float32
21,176,272
3,072,000
1.178291
0.159088
0
1.049683
0
0
39
0
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46
10
10
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15
191
5
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35
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5
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40
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2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
tf_efficientnet_b3_ns_Opset17.onnx
31.224238
0.068735
31.147831
31.332259
regnet_y_1_6gf_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1072:1x1000
1072:float32
3,222,376,320
1,776,000
98.222885
8.10553
0
0.006775
0
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27
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8
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
regnet_y_1_6gf_Opset17.onnx
13.262864
0.103046
13.115072
13.500614
resnet26t_Opset18_basic.onnx
x:1x3x256x256
x:float32
292:1x1000
292:float32
6,656,622,592
4,096,000
110.25
14.257812
0
0.015625
0
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8
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13
2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
resnet26t_Opset18.onnx
28.75236
0.098816
28.620664
28.935147
resnetaa50d_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
519:1x1000
519:float32
10,734,452,736
4,096,000
146.617188
14.267578
0
0.015625
0
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16
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8
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
resnetaa50d_Opset17.onnx
46.40932
14.118263
27.211872
74.748641
vit_large_patch32_384_Opset17_disable_all.onnx
x:1x3x384x384
x:float32
2133:1x1000
2133:float32
905,969,664
89,645,957,120
871.981875
0
117.487305
408.964966
0
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169
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1
7,950
32
32
512
2,449.998
205
amd
epyc
gcloud
vit_large_patch32_384_Opset17.onnx
1,080.162668
22.356675
1,060.92937
1,121.501854
blenderbotsmall_Opset17_basic.onnx
input_ids:1x128; attention_mask:1x128; decoder_input_ids:1x128
input_ids:int64; attention_mask:float32; decoder_input_ids:int64
3585:1x128x512; onnx::Reshape_1583:1x16x128x32; onnx::Reshape_1588:1x16x128x32; onnx::Reshape_1702:1x16x128x32; onnx::Reshape_1707:1x16x128x32; onnx::Reshape_1837:1x16x128x32; onnx::Reshape_1842:1x16x128x32; onnx::Reshape_1956:1x16x128x32; onnx::Reshape_1961:1x16x128x32; onnx::Reshape_2091:1x16x128x32; onnx::Reshape_20...
3585:float32; onnx::Reshape_1583:float32; onnx::Reshape_1588:float32; onnx::Reshape_1702:float32; onnx::Reshape_1707:float32; onnx::Reshape_1837:float32; onnx::Reshape_1842:float32; onnx::Reshape_1956:float32; onnx::Reshape_1961:float32; onnx::Reshape_2091:float32; onnx::Reshape_2096:float32; onnx::Reshape_2210:float32...
0
15,837,691,904
156.382919
0
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120
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2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
blenderbotsmall_Opset17.onnx
63.122561
0.10952
62.994171
63.34293
edgenext_x_small_Opset18.onnx
x:1x3x256x256
x:float32
1093:1x1000
1093:float32
89,985,448
966,945,792
177.562946
0.839432
10.141479
34.516857
6
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57
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3
8
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
edgenext_x_small_Opset18.onnx
8.347077
0.036355
8.291936
8.417033
coat_lite_mini_Opset17.onnx
x:1x3x224x224
x:float32
1825:1x1000
1825:float32
81,435,648
3,011,035,136
288.61198
0
12.152374
151.499199
0
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71
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4
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3
8
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
coat_lite_mini_Opset17.onnx
19.304837
0.087971
19.184321
19.485693
mobilenetv2_120d_Opset16_basic.onnx
x:1x3x224x224
x:float32
815:1x1000
815:float32
1,335,155,136
2,560,000
89.506752
0.244141
0
0.009766
0
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2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
mobilenetv2_120d_Opset16.onnx
23.402498
0.113563
23.207594
23.549315
convnext_large_384_in22ft1k_Opset17_basic.onnx
x:1x3x384x384
x:float32
1158:1x1000
1158:float32
6,262,603,776
195,692,519,424
1,020.197021
0.849609
194.304199
1,930.538452
0
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intel
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93
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7,950
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1,024
2,449.998
205
amd
epyc
gcloud
xcit_nano_12_p16_224_dist_Opset17.onnx
17.342182
0.074285
17.250289
17.435319
vit_relpos_base_patch16_clsgap_224_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1640:1x1000
1640:float32
231,211,008
34,234,165,248
489.636371
0.577156
69.343872
339.131264
0
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2
514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
vit_relpos_base_patch16_clsgap_224_Opset17.onnx
129.475467
0.474436
129.128402
130.78073
dpn98_Opset16_extended.onnx
x:1x3x224x224
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1669:1x1000
1669:float32
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217.4375
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gcloud
dpn98_Opset16.onnx
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deit_base_patch16_224_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1089:1x1000
1089:float32
231,211,008
34,896,445,440
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71.640747
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
deit_base_patch16_224_Opset17.onnx
69.441876
0.118491
69.220651
69.616968
ecaresnet101d_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
1852:1x1000
1852:float32
16,077,998,924
4,096,000
288.835327
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
ecaresnet101d_Opset17.onnx
76.020521
1.063115
75.279092
78.949592
dla102x2_Opset18.onnx
x:1x3x224x224
x:float32
1008:1x1000
1008:float32
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514,964
4,608
3,072
196,608
2,300
717
intel
xeon_plat
bluehive
dla102x2_Opset18.onnx
42.412537
1.008087
41.846372
45.183208
convnext_small_Opset17_torch_hub_extended.onnx
x:1x3x224x224
x:float32
1086:1x1000
1086:float32
718,695,936
16,648,728,576
173.609253
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intel
xeon_plat
bluehive
convnext_small_Opset17_torch_hub.onnx
88.999721
1.892583
87.954897
94.319691
regnety_004_Opset17_disable_all.onnx
x:1x3x224x224
x:float32
654:1x1000
654:float32
802,805,696
880,000
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514,964
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intel
xeon_plat
bluehive
regnety_004_Opset17.onnx
12.135786
0.086869
12.005984
12.2863
resnetv2_50x3_bitm_Opset16_basic.onnx
x:1x3x448x448
x:float32
1837:1x1000
1837:float32
288,621,428,736
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amd
epyc
gcloud
resnetv2_50x3_bitm_Opset16.onnx
3,388.76314
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marian_Opset18_basic.onnx
input_ids:1x128; attention_mask:1x128; decoder_input_ids:1x128
input_ids:int64; attention_mask:float32; decoder_input_ids:int64
2645:1x128x512; onnx::Reshape_1187:1x8x128x64; onnx::Reshape_1192:1x8x128x64; onnx::Reshape_1306:1x8x128x64; onnx::Reshape_1311:1x8x128x64; onnx::Reshape_1435:1x8x128x64; onnx::Reshape_1440:1x8x128x64; onnx::Reshape_1554:1x8x128x64; onnx::Reshape_1559:1x8x128x64; onnx::Reshape_1683:1x8x128x64; onnx::Reshape_1688:1x8x12...
2645:float32; onnx::Reshape_1187:float32; onnx::Reshape_1192:float32; onnx::Reshape_1306:float32; onnx::Reshape_1311:float32; onnx::Reshape_1435:float32; onnx::Reshape_1440:float32; onnx::Reshape_1554:float32; onnx::Reshape_1559:float32; onnx::Reshape_1683:float32; onnx::Reshape_1688:float32; onnx::Reshape_1802:float32...
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amd
epyc
gcloud
marian_Opset18.onnx
79.755131
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deit3_large_patch16_384_in21ft1k_Opset17_disable_all.onnx
x:1x3x384x384
x:float32
2229:1x1000
2229:float32
905,969,664
377,602,752,512
3,668.15766
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514,964
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intel
xeon_plat
bluehive
deit3_large_patch16_384_in21ft1k_Opset17.onnx
705.445073
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crossvit_small_240_Opset16.onnx
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x:float32
2833:1x1000
2833:float32
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514,964
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intel
xeon_plat
bluehive
crossvit_small_240_Opset16.onnx
47.629524
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End of preview. Expand in Data Studio

ONNX-Predict Dataset

This dataset is derived from ONNX-Predict, a dataset of ONNX model inference performance measurements collected from models hosted on the ONNX Model Zoo.

It contains inference data for 2,300+ ONNX models. Each model was optimized into four variants, and inference measurements were collected across 16 machines with different CPU core counts, memory sizes, cache configurations, and CPU providers.

Dataset Overview

Each row in the dataset combines three groups of features:

  1. Graph features describing the ONNX model structure and estimated data movement
  2. Hardware features describing the machine used for inference
  3. Inference measurements describing observed runtime performance

Features

The dataset includes:

  • Graph features: 133 features

    • Node counts: 127 features
    • Data movement and compute estimates: 6 features
  • Hardware features: 9 features

  • Inference data: 4 target/measurement columns

Graph Features

Graph features describe the structure and computational characteristics of each ONNX model.

Node Count Features

There are 127 node-count features, grouped by operation type.

Convolution Operations

Examples include:

  • Conv
  • QLinearConv
  • ConvTranspose

Matrix Multiplication Operations

Examples include:

  • MatMul
  • Gemm

Elementwise Operations

Examples include:

  • Relu
  • Sigmoid
  • Tanh
  • Mul
  • Div

Reduction and Pooling Operations

Examples include:

  • MaxPool
  • AveragePool
  • Other reduction-style operators

Normalization Operations

Examples include:

  • Softmax
  • BatchNormalization

Data Movement Operations

Examples include:

  • Transpose
  • Reshape
  • Other tensor layout or shape transformation operations

Other Operations

This group includes operators related to:

  • Control flow
  • Constants
  • Shape transformations
  • Boolean logic
  • Miscellaneous ONNX graph operations

Compute and Data Movement Features

The dataset also includes estimated compute and data movement features:

  • conv_flops
  • matmul_flops
  • elementwise_mb
  • reduction_mb
  • movement_mb
  • normalization_mb

These features provide additional information about the expected computational and memory behavior of each model.

Hardware Features

Hardware features describe the machine used to run inference.

Available hardware features include:

  • memory_mb
  • l1d_cache_kb
  • l1i_cache_kb
  • l2_cache_kb
  • base_clock_mhz
  • num_cores
  • memory_bandwidth_gbs
  • cpu_provider

The cpu_provider field identifies the CPU vendor, such as:

  • amd
  • intel

Inference Data

Inference measurements summarize observed runtime latency for each model, optimization variant, and hardware configuration.

The dataset includes the following inference columns:

  • average_ms
  • stddev_ms
  • min_ms
  • max_ms

All latency measurements are reported in milliseconds.

Source

The dataset is based on data from the ONNX-Predict project:

https://github.com/sidnarsipur/onnx-predict

License

This dataset is released under the MIT License.

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