clark-swin2sr-lightweight-x2-textstudy

Clark Swin2SR ×2 — text-robustness candidate v2 (realistic UI inputs)

A retrained Swin2SR ×2 super-resolution model, continued from the prior text-robustness candidate with a broader degradation battery and a larger dense realistic-text corpus. This revision (step-27,500) is the dominant checkpoint of the v2 run: it improves on the previous ternary release across all 12 measured realistic degradations.

This is a candidate revision. It is a strict fidelity improvement over the prior ternary release on a realistic input distribution, but it does not replace the promoted G3 artifact and does not claim q4 non-inferiority.

What changed vs the previous candidate

1. Broader degradation battery (12 total). Added sharpening halos (unsharp-mask overshoot) and gamma/color shifts to the existing set (bicubic/lanczos/bilinear/area + 4:2:0 chroma JPEG + WebP + blur + noise + fractional DPR + clean identity).
2. More training data. Added 40k dense realistic-text v2 images (22 fonts, 7 languages, code/UI copy, 8 themes, backdrop-blur panels) on top of the 44k real UI + 23.5k dense-text v1 corpus — 107,515 images total.
3. Checkpoint selection. The full v2 continuation ran to step-33,000, but later steps over-specialized to the new degradations and lost core ground. Step-27,500 is the dominant point: best mean fidelity with no degraded case.

Measured result (realistic UI distribution)

25 real UI screenshots × 12 degradations; PSNR, edge-PSNR, and OCR legibility (macOS Vision character accuracy). Paired against the previous ternary release (g2hard) and the shipped q4 reference.

• Wins PSNR and edge-PSNR on 12/12 degradations vs the previous ternary release — mean +0.288 dB, minimum +0.125 dB (no degraded case).
• Biggest gains on the newly-trained degradations: gamma +0.61 dB, sharpen +0.35 dB, blur +0.32 dB, WebP +0.30 dB.
• Dense ONNX is numerically identical to the PyTorch model (133 dB).
• Packed ternary native bundle passes browser-artifact conformance (76 dB).

Honest limits: the candidate still trails q4 on PSNR/edge (q4 is a larger, higher-precision reference); OCR legibility is roughly on par with q4 and the prior release (wins 4/12 vs q4). The win is fidelity + size, not OCR dominance.

Artifacts

• model.safetensors — dense weights (4.08 MB).
• onnx/model.onnx / onnx/model.onnx.gz — portable browser graph (input pixel_values, output reconstruction); gzip 1.68 MB (29.5% of q4).
• native/swin2sr-textstudy-x2.bdk.gz — packed ternary native bundle (320 KB, 5.6% of q4), conformance-passed.
• technical/quantization.json, technical/candidate-ledger.json, technical/browser-conformance.json — full quality receipts and per-degradation results.

Provenance

Source study: clark-quantize experiments/browser_swin2sr + realistic_text_eval. Training receipt and evaluation receipts are linked in technical/candidate-ledger.json.