README: add SAM 3 section (shapes, timings, SAM License)

README.md

@@ -2,11 +2,13 @@
 license: apache-2.0
 base_model:
   - hustvl/Moebius
+  - facebook/sam3
 tags:
   - executorch
   - react-native
   - on-device
   - inpainting
+  - segmentation
   - coreml
   - xnnpack
 ---
@@ -73,3 +75,37 @@ DA3-SMALL and DA3-BASE weights and code are Apache 2.0, and these exports inheri
       year={2025}
 }
 ```
+
+## sam3
+
+[SAM 3](https://huggingface.co/facebook/sam3) (Meta, 848M parameters), text-prompted instance segmentation ("segment every X"), exported to ExecuTorch for the [on-device SAM 3 demo app](https://github.com/NorbertKlockiewicz/on-device-sam3). Type a word, every instance is segmented on the phone.
+
+Layout: `sam3/coreml/` (iOS). All graphs are static at a 672x672 input (SAM 3 natively uses 1008; 672 finds the same instances on 6 of 7 test prompts and keeps the ViT on the Neural Engine). Core ML fp16.
+
+| file | runs | size | iPhone 16 Pro |
+|---|---|---|---|
+| `sam3/coreml/sam3_vision0_672_coreml.pte` | per photo | 230 MB | ~370 ms |
+| `sam3/coreml/sam3_vision1_672_coreml.pte` | per photo | 224 MB | ~330 ms |
+| `sam3/coreml/sam3_vision2_672_coreml.pte` | per photo | 224 MB | ~330 to 500 ms |
+| `sam3/coreml/sam3_vision3_672_coreml.pte` | per photo | 238 MB | ~330 ms |
+| `sam3/coreml/sam3_head_672_coreml.pte` | per prompt | 105 MB | ~170 to 300 ms |
+| `sam3/coreml/sam3_text_672_coreml.pte` | per prompt | 707 MB | ~11 to 20 ms |
+| `sam3/coreml/sam3_tokenizer.json` | | 4 MB | |
+
+- `sam3_vision0..3`: the 32-layer ViT in four 8-layer chunks (the ANE compiler rejects the whole ViT as one program). `vision0` takes `(1,3,672,672)` pixels in [-1,1] (`x/127.5 - 1`, photo stretched to square) and returns `(1,48,48,1024)`; `vision1` and `vision2` map that to the same shape; `vision3` adds the FPN neck and returns 3 maps `(1,256,192,192)`, `(1,256,96,96)`, `(1,256,48,48)`.
+- `sam3_text`: CLIP text encoder, `input_ids` and `attention_mask` `(1,32)` int64 (CLIP tokenizer, padded with 49407) -> `(1,32,256)`.
+- `sam3_head`: `(fpn0, fpn1, fpn2, text_features, attention_mask)` -> masks `(1,200,192,192)` logits, boxes `(1,200,4)` normalized cxcywh, logits `(1,200)`, presence `(1,1)`. Score = `sigmoid(logit) * sigmoid(presence)`. FPN position encodings are baked in.
+
+Verified end to end against PyTorch on real photos: identical instances and scores, mask IoU 0.994 to 1.0.
+
+These exports are derivatives of the SAM 3 weights and are distributed under Meta's SAM License, a copy of which is in `sam3/LICENSE`. They are not covered by the Apache 2.0 license of the other folders.
+
+```
+@misc{carion2025sam3segmentconcepts,
+      title={SAM 3: Segment Anything with Concepts},
+      author={Nicolas Carion and others},
+      year={2025},
+      eprint={2511.16719},
+      archivePrefix={arXiv}
+}
+```