ONNX model produces worse result than Pytorch counterpart

So I have the following for the Pytorch prediction for a finetuned model with only bounding boxes.

## Pytorch Prediction
```
bbox = [1055, 412, 1286, 991]
bbox = np.array(bbox)

predictor = SamPredictor(sam)
predictor.set_image(image)
masks, scores, logits = predictor.predict(
box=bbox,
multimask_output=False,
)
plt.imshow(masks[0])
```

and get the image like this (which is correct)

## ONNX Prediction
```
# Convert to ONNX
onnx_model = SamOnnxModel(sam, return_single_mask=True)

embed_size = sam.prompt_encoder.image_embedding_size
dummy_inputs = {
"image_embeddings": torch.randn(1, sam.prompt_encoder.embed_dim, *embed_size, dtype=torch.float),
"point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
"point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
"mask_input": torch.randn(1, 1, *([4 * x for x in embed_size]), dtype=torch.float),
"has_mask_input": torch.tensor([1], dtype=torch.float),
"orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
}

with open(model_output_path, "wb") as f:
torch.onnx.export(
onnx_model,
tuple(dummy_inputs.values()),
f,
export_params=True,
verbose=False,
opset_version=15,
do_constant_folding=True,
input_names=list(dummy_inputs.keys()),
output_names=["masks", "iou_predictions", "low_res_masks"],
dynamic_axes={
"point_coords": {1: "num_points"}, "point_labels": {1: "num_points"},
},
)

# Start onnx
ort_session = onnxruntime.InferenceSession(model_output_path)

# Encode bounding box
onnx_box_coords = input_box.reshape(2, 2)
onnx_box_labels = np.array([2, 3])

onnx_coord = onnx_box_coords[None, :, :]
onnx_label = onnx_box_labels[None, :].astype(np.float32)
onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)

onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
onnx_has_mask_input = np.array([0], dtype=np.float32)

ort_inputs = {
"image_embeddings": image_embedding,
"point_coords": onnx_coord,
"point_labels": onnx_label,
"mask_input": onnx_mask_input,
"has_mask_input": onnx_has_mask_input,
"orig_im_size": np.array(image.shape[:2], dtype=np.float32)
}

# Predict
masks, _, _ = ort_session.run(None, ort_inputs)
masks = masks > predictor.model.mask_threshold
plt.imshow(masks[0][0])
```

and get this wrong one

Any possible idea why?

Start by comparing the PyTorch SamPredictor path with the exported SamOnnxModel and the onnxruntime session shown in the issue. Check the transformed box coordinates, image_embedding, mask inputs, orig_im_size, and output thresholding for parity, then reproduce the mismatch with the supplied examples. Done means identifying the discrepancy and obtaining matching masks or documenting a confirmed export/runtime limitation.

Written by the indexing model from the issue text.