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README.md
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## Available Models
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### Anzhc's Face segmentation:
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full description and examples here: https://civitai.com/models/342514/anzhcs-beautifeyer-or-yolov8-or-adetailer-model
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## Available Models
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### Anzhc's Face segmentation:
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Series of models aiming at detecting and segmenting face accurately.
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| Model | Target | mAP 50 | mAP 50-95 |Classes |Training Resolution|
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| --------------------------- | --------------------- | ----------------------------- | ------------------------- |---------------|-------------------|
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| Anzhc Face -seg.pt | Face: illustration, real | LOST DATA | LOST DATA |2(male, female)| 640|
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| Anzhc Face -seg-hd.pt | Face: illustration, real | LOST DATA | LOST DATA |2(male, female)|1024|
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| Anzhc Face seg 640 v2 y8n.pt | Face: illustration, real | 0.872(box),0.872(mask) | 0.835(box),0.752(mask)|1(face) | 640|
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| Anzhc Face seg 768 v2 y8n.pt | Face: illustration, real | 0.86(box),0.86(mask) | 0.81(box),0.726(mask) |1(face) | 768|
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| Anzhc Face seg 768MS v2 y8n.pt | Face: illustration, real | 0.866(box),0.866(mask) | 0.816(box),0.72(mask) |1(face) | 768|(Multi-scale)|
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| Anzhc Face seg 1024 v2 y8n.pt | Face: illustration, real | 0.872(box),0.872(mask) | 0.804(box),0.726(mask)|1(face) | 1024|
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Take those stats with a grain of salt, since im pretty sure i re-scrambled dataset partition after training those models ages ago.
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Benchmark was performed in 640px.
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Difference in v2 models are only in their target resolution, so their performance spread is marginal.
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/--UNDER CONSTRUCTION--/
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