docs: add note about contiguous inputs

README.md

@@ -81,6 +81,39 @@ This model performs **instance segmentation**, outputting both bounding boxes an
 
 Output includes detection results and mask coefficients.
 
+
+## Troubleshooting
+
+### Low confidence / incorrect outputs with non-contiguous inputs
+
+If your outputs look wrong (for object-detection models this can show up as all confidences capped around **~0.20 / 20%** and **no detections**), ensure the input tensor passed to ExecuTorch is **contiguous**.
+
+Example:
+
+```python
+import torch
+
+# img_hwc: float32 HWC image (e.g. RGB) in [0, 1]
+x = torch.from_numpy(img_hwc).permute(2, 0, 1).unsqueeze(0)  # NCHW (often non-contiguous)
+x = x.contiguous()  # IMPORTANT
+
+outputs = method.execute([x])
+```
+
+Detection symptom example (before fix):
+
+```
+Confidence range: [0.0004, 0.2012]
+Detections: 0
+```
+
+After fix (`.contiguous()`):
+
+```
+Confidence range: [0.0001, 0.9589]
+Detections: 12
+```
+
 ## License
 
 This model is released under AGPL-3.0 license. See [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) for more details.