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README.md
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---
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## Available Models
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### Anzhc's Face segmentation:
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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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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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---
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## Available Models
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### Anzhc's Face segmentation:
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#### Universal:
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Series of models aiming at detecting and segmenting face accurately. Trained on closed dataset i annotated myself.
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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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Difference in v2 models are only in their target resolution, so their performance spread is marginal.
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#### Real Face, gendered:
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Trained only on real photos for the most part, so will perform poorly with illustrations, but is gendered, and can be used for male/female detection stack.
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| Model | Target | mAP 50 | mAP 50-95 |Classes |Training Resolution|
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| --------------------------- | --------------------- | ----------------------------- | ------------------------- |---------------|-------------------|
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| Anzhcs ManFace v02 1024 y8n.pt | Face: real | 0.883(box),0.883(mask) | 0.778(box), 0.704(mask) |1(face)|1024|
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| Anzhcs WomanFace v05 1024 y8n.pt | Face: real | 0.82(box),0.82(mask) | 0.713(box), 0.659(mask) |1(face)|1024|
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Benchmark was performed in 640px.
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/--UNDER CONSTRUCTION--/
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