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Add CoreML benchmarks

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Přemysl Eric Janouch 2024-01-19 15:32:10 +01:00
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deeptagger/README.adoc
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@ -49,11 +49,11 @@ Options
--threshold 0.1:: --threshold 0.1::
Output weight threshold. Needs to be set very high on ML-Danbooru models. Output weight threshold. Needs to be set very high on ML-Danbooru models.
Model benchmarks Model benchmarks (Linux)
---------------- ------------------------
These were measured on a machine with GeForce RTX 4090 (24G), 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, 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. 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|16|689 s
|ML-Danbooru Caformer dec-5-97527|1|829 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
|===