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diff --git a/deeptagger/README.adoc b/deeptagger/README.adoc index 8d65dfe..9a4d3aa 100644 --- a/deeptagger/README.adoc +++ b/deeptagger/README.adoc @@ -49,11 +49,11 @@ Options --threshold 0.1:: Output weight threshold. Needs to be set very high on ML-Danbooru models. -Model benchmarks ----------------- -These were measured on a machine with GeForce RTX 4090 (24G), +Model benchmarks (Linux) +------------------------ +These were measured with ORT 1.16.3 on a machine with GeForce RTX 4090 (24G), and Ryzen 9 7950X3D (32 threads), on a sample of 704 images, -which took over eight hours. +which took over eight hours. Times include model loading. There is room for further performance tuning. @@ -128,3 +128,85 @@ CPU inference |ML-Danbooru Caformer dec-5-97527|16|689 s |ML-Danbooru Caformer dec-5-97527|1|829 s |=== + +Model benchmarks (macOS) +------------------------ +These were measured with ORT 1.16.3 on a MacBook Pro, M1 Pro (16GB), +macOS Ventura 13.6.2, on a sample of 179 images. Times include model loading. + +There was often significant memory pressure and swapping, +which may explain some of the anomalies. CoreML often makes things worse, +and generally consumes a lot more memory than pure CPU execution. + +The kernel panic was repeatable. + +GPU inference +~~~~~~~~~~~~~ +[cols="<,>,>", options=header] +|=== +|Model|Batch size|Time +|DeepDanbooru|1|24 s +|DeepDanbooru|8|31 s +|DeepDanbooru|4|33 s +|WD v1.4 SwinV2 v2 (batch)|4|71 s +|WD v1.4 SwinV2 v2 (batch)|1|76 s +|WD v1.4 ViT v2 (batch)|4|97 s +|WD v1.4 ViT v2 (batch)|8|97 s +|ML-Danbooru TResNet-D 6-30000|8|100 s +|ML-Danbooru TResNet-D 6-30000|4|101 s +|WD v1.4 ViT v2 (batch)|1|105 s +|ML-Danbooru TResNet-D 6-30000|1|125 s +|WD v1.4 ConvNeXT v2 (batch)|8|126 s +|WD v1.4 SwinV2 v2 (batch)|8|127 s +|WD v1.4 ConvNeXT v2 (batch)|4|128 s +|WD v1.4 ConvNeXTV2 v2 (batch)|8|132 s +|WD v1.4 ConvNeXTV2 v2 (batch)|4|133 s +|WD v1.4 ViT v2|1|146 s +|WD v1.4 ConvNeXT v2 (batch)|1|149 s +|WD v1.4 ConvNeXTV2 v2 (batch)|1|160 s +|WD v1.4 MOAT v2 (batch)|1|165 s +|WD v1.4 SwinV2 v2|1|166 s +|WD v1.4 ConvNeXT v2|1|273 s +|WD v1.4 MOAT v2|1|273 s +|WD v1.4 ConvNeXTV2 v2|1|340 s +|ML-Danbooru Caformer dec-5-97527|1|551 s +|ML-Danbooru Caformer dec-5-97527|4|swap hell +|ML-Danbooru Caformer dec-5-97527|8|swap hell +|WD v1.4 MOAT v2 (batch)|4|kernel panic +|=== + +CPU inference +~~~~~~~~~~~~~ +[cols="<,>,>", options=header] +|=== +|Model|Batch size|Time +|DeepDanbooru|8|54 s +|DeepDanbooru|4|55 s +|DeepDanbooru|1|75 s +|WD v1.4 SwinV2 v2 (batch)|8|93 s +|WD v1.4 SwinV2 v2 (batch)|4|94 s +|ML-Danbooru TResNet-D 6-30000|8|97 s +|WD v1.4 SwinV2 v2 (batch)|1|98 s +|ML-Danbooru TResNet-D 6-30000|4|99 s +|WD v1.4 SwinV2 v2|1|99 s +|WD v1.4 ViT v2 (batch)|4|111 s +|WD v1.4 ViT v2 (batch)|8|111 s +|WD v1.4 ViT v2 (batch)|1|113 s +|WD v1.4 ViT v2|1|113 s +|ML-Danbooru TResNet-D 6-30000|1|118 s +|WD v1.4 ConvNeXT v2 (batch)|8|124 s +|WD v1.4 ConvNeXT v2 (batch)|4|125 s +|WD v1.4 ConvNeXTV2 v2 (batch)|8|129 s +|WD v1.4 ConvNeXT v2|1|130 s +|WD v1.4 ConvNeXTV2 v2 (batch)|4|131 s +|WD v1.4 MOAT v2 (batch)|8|134 s +|WD v1.4 ConvNeXTV2 v2|1|136 s +|WD v1.4 MOAT v2 (batch)|4|136 s +|WD v1.4 ConvNeXT v2 (batch)|1|146 s +|WD v1.4 MOAT v2 (batch)|1|156 s +|WD v1.4 MOAT v2|1|156 s +|WD v1.4 ConvNeXTV2 v2 (batch)|1|157 s +|ML-Danbooru Caformer dec-5-97527|4|241 s +|ML-Danbooru Caformer dec-5-97527|8|241 s +|ML-Danbooru Caformer dec-5-97527|1|262 s +|=== |