Image Feature Extraction
ONNX
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@@ -31,7 +31,7 @@ This model was created to close the gap between accurate text enhancement and th
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  - **Parameters:** 1,944,168 (~1.9 MB int8)
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  - **Input:** RGB text line strip, height 48 px; width multiple of 16 (pad before inference, crop after)
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  - **License:** Apache-2.0
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- -
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  ### Training & Evaluation
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  Trained from scratch on 50,000 synthetic strips from the Versta Glyphmatte dataset (5 new strips per step × 20k steps) with Adam + OneCycleLR, dice/BCE losses on matte and weight plus colour regression losses. Exported to ONNX with inlined weights (fp32) and an fp16 half-precision variant; validated against the fp32 PyTorch reference across four strip widths (figure-of-merit: max |Δ| ≤ 2.2e-06) and evaluated against a fixed validation shard (per-pixel IoU for the matte, AUC for weight).
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  - **Parameters:** 1,944,168 (~1.9 MB int8)
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  - **Input:** RGB text line strip, height 48 px; width multiple of 16 (pad before inference, crop after)
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  - **License:** Apache-2.0
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+
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  ### Training & Evaluation
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  Trained from scratch on 50,000 synthetic strips from the Versta Glyphmatte dataset (5 new strips per step × 20k steps) with Adam + OneCycleLR, dice/BCE losses on matte and weight plus colour regression losses. Exported to ONNX with inlined weights (fp32) and an fp16 half-precision variant; validated against the fp32 PyTorch reference across four strip widths (figure-of-merit: max |Δ| ≤ 2.2e-06) and evaluated against a fixed validation shard (per-pixel IoU for the matte, AUC for weight).
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