rewrite Why section: academic tone, trainer motivation, community credit

README.md

@@ -41,32 +41,47 @@ Part of the **BODY** suite by **Philip Hofmann** — networks built to be
 
 ## Why NERVE?
 
-There are many SR networks already. Here's the honest case for NERVE — and when
-to pick something else.
-
-**One-line pitch:** a ~1.8M-param, pure-conv SR net that trains with plain
-`train.py`, exports to dynamic ONNX on the first try, and converts to ncnn —
-with no fusion step, no variants, no custom ops.
-
-**Why it's worth your time:**
-- **One file, one variant.** The architecture is a single readable file and the
-  model is one size. No S/M/L matrix, no fused-vs-unfused checkpoints, nothing
-  to fuse before deploy — **what you train is what you ship.**
+There are strong and established SR networks already. This is the honest case
+for when NERVE is worth your time — and when it is not.
+
+This project comes out of a trainer's frustration rather than a benchmark goal.
+After training hundreds of SISR models (see
+[Phhofm/models](https://github.com/Phhofm/models)), the recurring annoyance was
+networks that felt like *paper* networks: they chase leaderboard metrics with
+techniques that leave you juggling an unfused and a fused checkpoint, or whose
+dynamic-ONNX conversion is fragile. This network is the opposite of that — built
+so a trainer can pick it up, train it, and convert it to dynamic ONNX without
+headaches.
+
+**Strengths of NERVE**
+- **Real-world usability over metric chasing.** Trained on a CC0 dataset with
+  the full Real-ESRGAN OTF degradation pipeline; the OTF GAN model restores
+  degraded images, not just clean bicubic downsamples.
+- **One file, one variant.** A single readable architecture file and one model
+  size. No S/M/L matrix, no fused-vs-unfused checkpoints, nothing to fuse before
+  deploy — what you train is what you ship.
 - **Export just works.** Dynamic-shape ONNX (verified, ORT parity ~1e-5) and a
   clean ncnn conversion. Standard ops only (`Conv / Add / ReLU / DepthToSpace /
   Resize`).
 - **Boring, stable training.** Norm-free, bf16 AMP, no gradient clipping, a
   single Charbonnier loss for the base stage.
-- **Small and fast.** ~1.8M params — SPAN-class size but conv-only, so it fits
+- **Small and fast.** ~1.8M params — SPAN-class size but conv-only, suited to
   mobile, edge, web and game engines.
-- **Real-world first.** Trained on a CC0 dataset with the full Real-ESRGAN OTF
-  degradation pipeline; the OTF GAN model restores degraded images, not just
-  clean bicubic downsamples.
 - **Maintainable for years.** One op set, no exotic dependencies, ~90-line arch.
 
-**When to pick something else:** if you want maximum PSNR/LPIPS on academic
-benchmarks, or a big transformer, NERVE isn't trying to win that — use HEART.
-NERVE wins on *simplicity, deployability and hassle-free training*.
+**When to pick something else.** If the goal is maximum PSNR/LPIPS on academic
+benchmarks, or a large transformer, NERVE is not aiming there — use HEART, HAT,
+ATD, DAT2, or one of the community networks below. NERVE aims at simplicity,
+deployability and hassle-free training.
+
+**Credit where it is due.** None of this exists in a vacuum. SISR has many
+strong architectures and generous community members, for example HAT, ATD and
+DAT2, the community network work of [umzi](https://github.com/umzi2) and
+[Kim2091](https://github.com/Kim2091), fdat by
+[sharekhan](https://github.com/stinkybread), and
+[the-database](https://github.com/the-database) (traiNNer-redux, its benchmarks
+and its models). This list is far from complete — apologies to anyone not named;
+the SISR community has a great many people contributing a great deal.
 
 ---