fixed the readmes

README.md

@@ -9,27 +9,30 @@ an RT-DETR-based **document layout detector** (~33M params), for the
 exported to `.pte` for the **ExecuTorch** runtime (XNNPACK, CoreML, Vulkan). It finds and
 classifies document regions — titles, paragraphs, tables, figures, formulas, headers/footers,
 etc. — and is a companion to
-[`react-native-executorch-paddleocr`](https://huggingface.co/software-mansion/react-native-executorch-PP-OCRv6).
+[`react-native-executorch-pp-ocrv6`](https://huggingface.co/software-mansion/react-native-executorch-pp-ocrv6).
 
 If you'd like to run these models in your own ExecuTorch runtime, refer to the
 [official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
 
-The `.pte` is a pure tensor→tensor function; all pre/post-processing (resize, normalize, score
-threshold, box convert) is the client's job.
+The `.pte` is a pure tensor→tensor function; pre-processing (resize, normalize) and the final
+score threshold are the client's job.
 
 ## Output contract
 
-A single static method `forward`, fixed input (no buckets):
+A single **fixed-shape** method `forward` (shape also declared in `config.json`; no
+shape-discovery companion methods on this model). The RT-DETR box decode is **baked into the
+graph** — outputs are ready-to-threshold:
 
 ```
-in   [1, 3, 800, 800]          # RGB, ImageNet-normalized (x/255 - mean)/std
-out  logits     [1, 300, 25]   # 25 layout classes, per query (apply sigmoid)
-     pred_boxes [1, 300, 4]    # (cx, cy, w, h), normalized [0,1]
+in   [1, 3, 800, 800]     # RGB, ImageNet-normalized by the client: (x/255 - mean)/std
+out  boxes   [300, 4]     # (x1, y1, x2, y2) in 800×800 model-input pixel space
+     scores  [300]        # max-class sigmoid score per query
+     classes [300]        # float class index per query (argmax)
 ```
 
-PP-DocLayoutV3 is a **DETR set-prediction** model → **no NMS**. Post-processing is just:
-`score = sigmoid(logits)`, keep queries above a threshold, convert `(cx,cy,w,h) → (x1,y1,x2,y2)`,
-scale to image size. Class names are in `labels.json` (index → label).
+PP-DocLayoutV3 is a **DETR set-prediction** model → **no NMS**. All 300 queries are returned;
+post-processing is just: keep rows with `score ≥ threshold`, scale boxes from the 800×800
+input space to your image, and map `classes[i]` through `labels.json` (index → label).
 
 ### Classes (25)
 
@@ -42,16 +45,14 @@ seal, table, text, vision_footnote` (some indices map to the same display label;
 
 | backend | target | precision | size | latency |
 |---|---|---|---|---|
-| `xnnpack` | CPU | fp32 | 132 MB | ~2.0 s (S24) |
-| `coreml` | Apple ANE | fp16 | 91 MB | ANE fp16 |
-| `vulkan` | Android GPU | fp16 (mixed-delegate) | **66 MB** | **~0.86 s (S24)** |
+| `xnnpack` | CPU | fp32 | 132 MB | ~2.0 s (Galaxy S24) |
+| `coreml` | Apple ANE | fp16 | 91 MB | ~50 ms (Apple M-series ANE) |
+| `vulkan` | Android GPU | fp16 (mixed-delegate) | **66 MB** | **~0.86 s (Galaxy S24)** |
 
 > **Vulkan is the recommended Android backend** — ~2.4× faster than XNNPACK and half the size.
 > It's mixed-delegate: most of RT-DETR runs fp16 on the GPU, while the box-head matmuls run on
-> XNNPACK (they delegate as `addmm`→`linear`). XNNPACK stays fp32 because RT-DETR's deformable
-> attention feeds non-contiguous tensors that int8/portable paths mis-handle.
-
-
+> XNNPACK (they delegate as `addmm`→`linear`). XNNPACK stays fp32 because int8/int4
+> quantization loses whole boxes on this model.
 
 ## Compatibility
 
@@ -61,4 +62,3 @@ the compatibility note in the
 [ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/main/runtime/COMPATIBILITY.md).
 If you work with React Native ExecuTorch, the library constants guarantee compatibility with the
 runtime used behind the scenes.
-