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deeptagger: add an example of how to use it 

And refer to CAFormer correctly.
This commit is contained in:
Přemysl Eric Janouch 2024-01-21 12:35:48 +01:00
parent 454cfd688c
commit aa65466a49
Signed by: p
GPG Key ID: A0420B94F92B9493
2 changed files with 25 additions and 14 deletions
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37
deeptagger/README.adoc
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@ -47,7 +47,18 @@ Options
--pipe:: --pipe::
Take input filenames from the standard input. Take input filenames from the standard input.
--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 higher on ML-Danbooru models.
Tagging galleries
-----------------
The appropriate invocation depends on your machine, and the chosen model.
Unless you have a powerful machine, or use a fast model, it may take forever.
$ find "$GALLERY/images" -type f \
| build/deeptagger --pipe -b 16 -t 0.5 \
models/ml_caformer_m36_dec-5-97527.model \
| sed 's|[^\t]*/||' \
| gallery tag "$GALLERY" caformer "ML-Danbooru CAFormer"
Model benchmarks (Linux) Model benchmarks (Linux)
------------------------ ------------------------
@ -65,14 +76,14 @@ GPU inference
|WD v1.4 ViT v2 (batch)|16|19 s |WD v1.4 ViT v2 (batch)|16|19 s
|DeepDanbooru|16|21 s |DeepDanbooru|16|21 s
|WD v1.4 SwinV2 v2 (batch)|16|21 s |WD v1.4 SwinV2 v2 (batch)|16|21 s
|ML-Danbooru Caformer dec-5-97527|16|25 s |ML-Danbooru CAFormer dec-5-97527|16|25 s
|WD v1.4 ViT v2 (batch)|4|27 s |WD v1.4 ViT v2 (batch)|4|27 s
|WD v1.4 SwinV2 v2 (batch)|4|30 s |WD v1.4 SwinV2 v2 (batch)|4|30 s
|DeepDanbooru|4|31 s |DeepDanbooru|4|31 s
|ML-Danbooru TResNet-D 6-30000|16|31 s |ML-Danbooru TResNet-D 6-30000|16|31 s
|WD v1.4 MOAT v2 (batch)|16|31 s |WD v1.4 MOAT v2 (batch)|16|31 s
|WD v1.4 ConvNeXT v2 (batch)|16|32 s |WD v1.4 ConvNeXT v2 (batch)|16|32 s
|ML-Danbooru Caformer dec-5-97527|4|32 s |ML-Danbooru CAFormer dec-5-97527|4|32 s
|WD v1.4 ConvNeXTV2 v2 (batch)|16|36 s |WD v1.4 ConvNeXTV2 v2 (batch)|16|36 s
|ML-Danbooru TResNet-D 6-30000|4|39 s |ML-Danbooru TResNet-D 6-30000|4|39 s
|WD v1.4 ConvNeXT v2 (batch)|4|39 s |WD v1.4 ConvNeXT v2 (batch)|4|39 s
@ -80,7 +91,7 @@ GPU inference
|WD v1.4 ConvNeXTV2 v2 (batch)|4|43 s |WD v1.4 ConvNeXTV2 v2 (batch)|4|43 s
|WD v1.4 ViT v2|1|43 s |WD v1.4 ViT v2|1|43 s
|WD v1.4 ViT v2 (batch)|1|43 s |WD v1.4 ViT v2 (batch)|1|43 s
|ML-Danbooru Caformer dec-5-97527|1|52 s |ML-Danbooru CAFormer dec-5-97527|1|52 s
|DeepDanbooru|1|53 s |DeepDanbooru|1|53 s
|WD v1.4 MOAT v2|1|53 s |WD v1.4 MOAT v2|1|53 s
|WD v1.4 ConvNeXT v2|1|54 s |WD v1.4 ConvNeXT v2|1|54 s
@ -110,7 +121,7 @@ CPU inference
|WD v1.4 ConvNeXTV2 v2|1|245 s |WD v1.4 ConvNeXTV2 v2|1|245 s
|WD v1.4 ConvNeXTV2 v2 (batch)|4|268 s |WD v1.4 ConvNeXTV2 v2 (batch)|4|268 s
|WD v1.4 ViT v2 (batch)|16|270 s |WD v1.4 ViT v2 (batch)|16|270 s
|ML-Danbooru Caformer dec-5-97527|4|270 s |ML-Danbooru CAFormer dec-5-97527|4|270 s
|WD v1.4 ConvNeXT v2 (batch)|1|272 s |WD v1.4 ConvNeXT v2 (batch)|1|272 s
|WD v1.4 SwinV2 v2 (batch)|4|277 s |WD v1.4 SwinV2 v2 (batch)|4|277 s
|WD v1.4 ViT v2 (batch)|4|277 s |WD v1.4 ViT v2 (batch)|4|277 s
@ -118,7 +129,7 @@ CPU inference
|WD v1.4 SwinV2 v2 (batch)|1|300 s |WD v1.4 SwinV2 v2 (batch)|1|300 s
|WD v1.4 SwinV2 v2|1|302 s |WD v1.4 SwinV2 v2|1|302 s
|WD v1.4 SwinV2 v2 (batch)|16|305 s |WD v1.4 SwinV2 v2 (batch)|16|305 s
|ML-Danbooru Caformer dec-5-97527|16|305 s |ML-Danbooru CAFormer dec-5-97527|16|305 s
|WD v1.4 MOAT v2 (batch)|4|307 s |WD v1.4 MOAT v2 (batch)|4|307 s
|WD v1.4 ViT v2|1|308 s |WD v1.4 ViT v2|1|308 s
|WD v1.4 ViT v2 (batch)|1|311 s |WD v1.4 ViT v2 (batch)|1|311 s
@ -126,7 +137,7 @@ CPU inference
|WD v1.4 MOAT v2|1|332 s |WD v1.4 MOAT v2|1|332 s
|WD v1.4 MOAT v2 (batch)|16|335 s |WD v1.4 MOAT v2 (batch)|16|335 s
|WD v1.4 MOAT v2 (batch)|1|339 s |WD v1.4 MOAT v2 (batch)|1|339 s
|ML-Danbooru Caformer dec-5-97527|1|352 s |ML-Danbooru CAFormer dec-5-97527|1|352 s
|=== |===
Model benchmarks (macOS) Model benchmarks (macOS)
@ -166,12 +177,12 @@ GPU inference
|WD v1.4 ConvNeXTV2 v2 (batch)|1|160 s |WD v1.4 ConvNeXTV2 v2 (batch)|1|160 s
|WD v1.4 MOAT v2 (batch)|1|165 s |WD v1.4 MOAT v2 (batch)|1|165 s
|WD v1.4 SwinV2 v2|1|166 s |WD v1.4 SwinV2 v2|1|166 s
|ML-Danbooru Caformer dec-5-97527|1|263 s |ML-Danbooru CAFormer dec-5-97527|1|263 s
|WD v1.4 ConvNeXT v2|1|273 s |WD v1.4 ConvNeXT v2|1|273 s
|WD v1.4 MOAT v2|1|273 s |WD v1.4 MOAT v2|1|273 s
|WD v1.4 ConvNeXTV2 v2|1|340 s |WD v1.4 ConvNeXTV2 v2|1|340 s
|ML-Danbooru Caformer dec-5-97527|4|445 s |ML-Danbooru CAFormer dec-5-97527|4|445 s
|ML-Danbooru Caformer dec-5-97527|8|1790 s |ML-Danbooru CAFormer dec-5-97527|8|1790 s
|WD v1.4 MOAT v2 (batch)|4|kernel panic |WD v1.4 MOAT v2 (batch)|4|kernel panic
|=== |===
@ -189,14 +200,14 @@ CPU inference
|WD v1.4 SwinV2 v2 (batch)|1|98 s |WD v1.4 SwinV2 v2 (batch)|1|98 s
|ML-Danbooru TResNet-D 6-30000|4|99 s |ML-Danbooru TResNet-D 6-30000|4|99 s
|WD v1.4 SwinV2 v2|1|99 s |WD v1.4 SwinV2 v2|1|99 s
|ML-Danbooru Caformer dec-5-97527|4|110 s |ML-Danbooru CAFormer dec-5-97527|4|110 s
|ML-Danbooru Caformer dec-5-97527|8|110 s |ML-Danbooru CAFormer dec-5-97527|8|110 s
|WD v1.4 ViT v2 (batch)|4|111 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)|8|111 s
|WD v1.4 ViT v2 (batch)|1|113 s |WD v1.4 ViT v2 (batch)|1|113 s
|WD v1.4 ViT v2|1|113 s |WD v1.4 ViT v2|1|113 s
|ML-Danbooru TResNet-D 6-30000|1|118 s |ML-Danbooru TResNet-D 6-30000|1|118 s
|ML-Danbooru Caformer dec-5-97527|1|122 s |ML-Danbooru CAFormer dec-5-97527|1|122 s
|WD v1.4 ConvNeXT v2 (batch)|8|124 s |WD v1.4 ConvNeXT v2 (batch)|8|124 s
|WD v1.4 ConvNeXT v2 (batch)|4|125 s |WD v1.4 ConvNeXT v2 (batch)|4|125 s
|WD v1.4 ConvNeXTV2 v2 (batch)|8|129 s |WD v1.4 ConvNeXTV2 v2 (batch)|8|129 s

2
deeptagger/download.sh
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@ -157,7 +157,7 @@ wd14 'WD v1.4 SwinV2 v2' 'SmilingWolf/wd-v1-4-swinv2-tagger-v2'
wd14 'WD v1.4 MOAT v2' 'SmilingWolf/wd-v1-4-moat-tagger-v2' wd14 'WD v1.4 MOAT v2' 'SmilingWolf/wd-v1-4-moat-tagger-v2'
# As suggested by author https://github.com/IrisRainbowNeko/ML-Danbooru-webui # As suggested by author https://github.com/IrisRainbowNeko/ML-Danbooru-webui
mldanbooru 'ML-Danbooru Caformer dec-5-97527' \ mldanbooru 'ML-Danbooru CAFormer dec-5-97527' \
448 'ml_caformer_m36_dec-5-97527.onnx' 448 'ml_caformer_m36_dec-5-97527.onnx'
mldanbooru 'ML-Danbooru TResNet-D 6-30000' \ mldanbooru 'ML-Danbooru TResNet-D 6-30000' \
640 'TResnet-D-FLq_ema_6-30000.onnx' 640 'TResnet-D-FLq_ema_6-30000.onnx'