roboflow/supervision · error · ValueError

`slice_wh` must be an int or a tuple of two positive integer

Error message

`slice_wh` must be an int or a tuple of two positive integers (slice_w, slice_h). Received: {slice_wh}

What it means

Raised by InferenceSlicer's _normalize_slice_wh when slice_wh is neither an int nor a 2-tuple of ints. The slicer tiles the image into cells of (slice_w, slice_h); any other type (float, string, list, 1- or 3-tuple) has no defined meaning, so it is rejected during construction.

Source

Thrown at src/supervision/detection/tools/inference_slicer.py:661

    def _normalize_slice_wh(
        slice_wh: int | tuple[int, int],
    ) -> tuple[int, int]:
        if isinstance(slice_wh, int):
            if slice_wh <= 0:
                raise ValueError(
                    f"`slice_wh` must be a positive integer. Received: {slice_wh}"
                )
            return slice_wh, slice_wh

        if isinstance(slice_wh, tuple) and len(slice_wh) == 2:
            width, height = slice_wh
            if width <= 0 or height <= 0:
                raise ValueError(
                    f"`slice_wh` values must be positive. Received: {slice_wh}"
                )
            return width, height

        raise ValueError(
            "`slice_wh` must be an int or a tuple of two positive integers "
            "(slice_w, slice_h). "
            f"Received: {slice_wh}"
        )

    @staticmethod
    def _normalize_overlap_wh(
        overlap_wh: int | tuple[int, int],
    ) -> tuple[int, int]:
        if isinstance(overlap_wh, int):
            if overlap_wh < 0:
                raise ValueError(
                    "`overlap_wh` must be a non negative integer. "
                    f"Received: {overlap_wh}"
                )
            return overlap_wh, overlap_wh

        if isinstance(overlap_wh, tuple) and len(overlap_wh) == 2:

View on GitHub (pinned to 7f254d9784)

Solutions

1. Pass an int (slice_wh=512) or an int 2-tuple (slice_wh=(512, 384)).
2. Coerce config values at load time: tuple(int(v) for v in cfg['slice_wh']) if it is a sequence, else int(cfg['slice_wh']).
3. Validate config schema before constructing the slicer.

Example fix

# before
slicer = sv.InferenceSlicer(callback=cb, slice_wh=[512, 512])  # ValueError

# after
slicer = sv.InferenceSlicer(callback=cb, slice_wh=(512, 512))

def normalize_slice_wh(v):
    if isinstance(v, (list, tuple)):
        v = tuple(int(x) for x in v)
    else:
        v = int(v)
    return v

slicer = sv.InferenceSlicer(callback=cb, slice_wh=normalize_slice_wh(cfg['slice_wh']))

def is_valid_slice_wh(v) -> bool:
    if isinstance(v, int):
        return v > 0
    return isinstance(v, tuple) and len(v) == 2 and all(isinstance(x, int) and x > 0 for x in v)

When it happens

Trigger: Constructing sv.InferenceSlicer(callback=..., slice_wh=512.0), slice_wh="512", slice_wh=[512, 512], or slice_wh=(512, 512, 3).

Common situations: slice_wh read from JSON/YAML config arrives as float or list; CLI argument parsing yields a string; accidentally passing image resolution or a channel-count tuple.

Related errors

• `overlap_wh` must be an int or a tuple of two non negative i
• `thread_workers` must be a positive integer. Received: {thre
• `batch_size` must be a positive integer. Received: {batch_si
• `slice_wh` must be a positive integer. Received: {slice_wh}
• `slice_wh` values must be positive. Received: {slice_wh}

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/bf624c78da7d80ce. Report an issue: GitHub.