This example benchmarks CompactMask, a new mask representation introduced in supervision that replaces dense (N, H, W) boolean arrays with a crop-scoped Run-Length Encoding (RLE). The benchmark demonstrates full API compatibility, massive memory savings, and order-of-magnitude annotation speedups — with no change to your existing Detections code.

Instance segmentation models return one boolean mask per detected object. supervision stores these as a stacked (N, H, W) numpy array.

For a 4K image with 1 000 detected objects:

1 000 x 3840 x 2160 x 1 byte = 8.3 GB

At this scale, typical pipelines crash with MemoryError before a single frame is annotated. Aerial imagery, satellite tiles, and high-density crowd scenes all hit this wall.

CompactMask stores each mask as a run-length encoding of its bounding-box crop rather than the full image canvas.

dense (N,H,W) mask   →   N x crop_RLE + N x (x1,y1) offset
8.3 GB               →   ~280 KB

The bounding boxes are already present in Detections.xyxy, so no extra metadata is required from the caller.

Assumptions used throughout the PR design analysis:

Crop-RLE beats Local Crop because it only encodes actual pixel runs, skipping the ~35% background pixels within each bounding box.

Required by MaskAnnotator, mask_iou_batch, merge(), etc. Dominant cost at 4K is allocating and zeroing a 8.29 MB array, which is identical across all in-memory formats once full materialisation is needed.

When callers need only the bounding-box region — MaskAnnotator crop-paint path, .area, contains_holes, filter_segments_by_distance:

Crop RLE's .crop() method powers the MaskAnnotator optimisation — it never allocates the full image canvas, which is the entire source of the annotation speedup.

At N=1 000 with 1 % overlap, bbox pre-filter reduces 499 500 candidate pairs to ~5 000 overlapping pairs — a ~2 000x reduction in pixel-level work.

Both formats compress extremely well; the deciding factors for Crop-RLE are:

1. ~3x smaller for masks that are themselves sparse within their bounding box.
2. COCO RLE interop path — crop RLE uses column-major (F-order) pixel scan, matching pycocotools; to interoperate, you still need to construct a full-image COCO RLE from the crop-scoped encoding (for example by padding/merging runs onto the full-image canvas, or by materialising the crop in the full image and re-encoding).
3. .area computed directly from run lengths — no materialisation, no allocation.

The main trade-off: crop-only decode is O(A) rather than O(1). For the common solid-fill segmentation mask this is negligible (<0.1 ms per mask).

This section walks through every Detections operation that touches masks and shows exactly why CompactMask is faster. All code snippets are taken from the actual implementation. Numbers use the FHD-200-50%-v600 scenario unless noted (1920 x 1080 image, 200 detections, each mask filling ~50% of the frame, 600-vertex polygons — a realistic hard case with dense fill and complex object boundaries).

At 50% fill on an FHD image each mask's bounding box covers a large portion of the frame, producing many RLE runs per row.

Dense stores one full-resolution bool array per mask:

N x H x W x 1 byte
200 x 1080 x 1920 x 1 = 414 MB

Compact stores three lightweight structures:

self._rles: list[npt.NDArray[np.int32]]  # N Python references to small int32 arrays
self._crop_shapes: npt.NDArray[np.int32]  # (N, 2) — crop (h, w) per mask
self._offsets: npt.NDArray[np.int32]  # (N, 2) — (x1, y1) origin per mask

Per-mask RLE size at 50% fill with 600-vertex polygons: ~4.7 KB (933 KB / 200). Per-mask dense size: 1920 x 1080 x 1 = 2.1 MB. Per-mask ratio: 2.1 MB / 4.7 KB = ~445x.

Scaled to N=200: 200 x 4.7 KB = ~933 KB of RLE data, plus _crop_shapes (1.6 KB) and _offsets (1.6 KB). Python list + array object overhead roughly doubles the footprint for small N.

At 5% fill with 8-vertex polygons, the ratio reaches 10 000x–20 000x because crops are tiny and RLEs are extremely short. The benchmark's 4K-200-5%-v8 scenario measures 21 786x (theory) / ~6 000x (malloc). The SAT-200-5%-v8 scenario reaches 62 968x theoretical.

Dense Detections.area reads every pixel of every mask:

# detection/core.py — dense path
return np.array([np.sum(mask) for mask in self.mask])
# N masks x H x W boolean sums = 200 x 2.1 M = 420 million reads

Compact delegates to _rle_area, which sums only the odd-indexed run lengths (the True-pixel runs) in each RLE:

# detection/compact_mask.py — _rle_area
return int(np.sum(rle[1::2]))

# detection/compact_mask.py — CompactMask.area
return np.array([_rle_area(r) for r in self._rles], dtype=np.int64)

At FHD-200-50%-v600, dense .area takes 84.66 ms; compact takes 0.48 ms — a 71x speedup. At SAT-200-20%-v128 the measured speedup reaches 1 204x because the dense array is 13.4 GB and each sum must scan the entire canvas.

Dense: masks[bool_array] triggers NumPy fancy indexing, which allocates a new (K, H, W) bool array and copies K full frames:

# detection/core.py — Detections.__getitem__
mask = (self.mask[index] if self.mask is not None else None,)
# For dense ndarray, numpy allocates (K, 2160, 3840) and memcpy's K frames

Compact CompactMask.__getitem__ converts the boolean index to integer positions and builds a new CompactMask from Python list indexing and NumPy fancy indexing on small (N, 2) arrays:

# detection/compact_mask.py — CompactMask.__getitem__
if isinstance(index, np.ndarray) and index.dtype == bool:
    idx_arr = np.where(index)[0]
# ...
new_rles = [self._rles[int(i)] for i in idx_arr]
new_crop_shapes: npt.NDArray[np.int32] = self._crop_shapes[idx_arr]
new_offsets: npt.NDArray[np.int32] = self._offsets[idx_arr]
return CompactMask(new_rles, new_crop_shapes, new_offsets, self._image_shape)

At FHD-200-50%-v600, dense filter takes 14.56 ms; compact takes 0.03 ms — a 500x speedup. At SAT-200-20%-v128 the speedup reaches 14 757x.

Dense: for each mask, MaskAnnotator indexes the full (H, W) array and applies a boolean mask across the entire scene:

# annotators/core.py — dense path
mask = np.asarray(detections.mask[detection_idx], dtype=bool)
colored_mask[mask] = color.as_bgr()

Each detections.mask[detection_idx] for a dense array yields a full (H, W) view, and the boolean indexing scans all pixels.

Compact: the annotator detects CompactMask and paints only the crop region:

# annotators/core.py — compact path
x1 = int(compact_mask.offsets[detection_idx, 0])
y1 = int(compact_mask.offsets[detection_idx, 1])
crop_m = compact_mask.crop(detection_idx)
crop_h, crop_w = crop_m.shape
colored_mask[y1 : y1 + crop_h, x1 : x1 + crop_w][crop_m] = color.as_bgr()

compact_mask.crop() decodes the RLE into a (crop_h, crop_w) array. At FHD-200-50%-v600, dense annotate takes 848.95 ms; compact takes 32.67 ms — a 22x speedup. At SAT-200-20%-v128 the speedup reaches 89x.

Dense mask_iou_batch on N=200, FHD:

# detection/utils/iou_and_nms.py — _mask_iou_batch_split
intersection_area = np.logical_and(masks_true[:, None], masks_detection).sum(
    axis=(2, 3)
)
# shape (200, 200, 1080, 1920) — ~80 billion boolean ops
# .sum(axis=(2,3)) for intersection counts
# memory_limit splits this into chunks capped at 5 GB scratch

Compact compact_mask_iou_batch — three layered optimisations:

1. Vectorised bbox pre-filter — O(N²) array ops, zero decoding

ix1: npt.NDArray[np.int32] = np.maximum(x1a[:, None], x1b[None, :])
iy1: npt.NDArray[np.int32] = np.maximum(y1a[:, None], y1b[None, :])
ix2: npt.NDArray[np.int32] = np.minimum(x2a[:, None], x2b[None, :])
iy2: npt.NDArray[np.int32] = np.minimum(y2a[:, None], y2b[None, :])
bbox_overlap: npt.NDArray[np.bool_] = (ix1 <= ix2) & (iy1 <= iy2)

At 5% fill, two random masks overlap with probability ~4%. ~96% of the N² pairs get IoU = 0 for free — no pixel work at all.

2. Sub-crop decode — compare only the intersection region

ox_a, oy_a = int(x1a[i]), int(y1a[i])
sub_a = crops_a[i][ly1 - oy_a : ly2 - oy_a + 1, lx1 - ox_a : lx2 - ox_a + 1]

ox_b, oy_b = int(x1b[j]), int(y1b[j])
sub_b = crops_b[j][ly1 - oy_b : ly2 - oy_b + 1, lx1 - ox_b : lx2 - ox_b + 1]

inter = int(np.logical_and(sub_a, sub_b).sum())

The intersection sub-region of two overlapping crops is typically far smaller than the full frame.

3. Crop caching — each mask decoded at most once

if i not in crops_a:
    crops_a[i] = masks_true.crop(i)

Area is obtained from _rle_area (sum odd-indexed runs), never touching the pixel grid:

areas_a: npt.NDArray[np.int64] = masks_true.area

At FHD-200-50%-v600, dense IoU takes 23 915 ms; compact takes 51.58 ms — a 446x speedup. At 5% fill / sparse scenarios the speedup is even larger because fewer bbox pairs overlap.

At 20% fill the gaps close — more pairs overlap, larger crops — speedup drops toward the lower end of the range.

Both dense and compact paths now call mask_iou_batch(masks, masks) directly, computing exact mask IoU on the original (unresized) masks. There is no intermediate resize step.

# detection/utils/iou_and_nms.py — NMS (both paths)
ious = mask_iou_batch(masks, masks, overlap_metric)

mask_iou_batch dispatches internally: when passed a CompactMask it calls compact_mask_iou_batch, applying all three IoU optimisations (bbox pre-filter, sub-crop decode, crop caching). When passed a dense ndarray it runs the chunked pixel-AND path.

All three IoU optimisations apply to the compact path:

At FHD-200-50%-v600, dense NMS takes 5 231 ms; compact takes 48.15 ms — a 481x speedup. Dense IoU/NMS is skipped for scenarios above 1 GB (4K-200 and SAT-200 tiers); compact NMS still runs on those.

Dense: np.vstack allocates a new (N1+N2, H, W) array and copies both halves:

# detection/core.py — dense merge path
return np.vstack([np.asarray(m) for m in masks])
# Merging two 100-mask sets at FHD: 2 x 100 x 2.1 MB = 414 MB copied

Compact: CompactMask.merge extends a Python list and concatenates two small int32 arrays:

# detection/compact_mask.py — CompactMask.merge
new_rles: list[npt.NDArray[np.int32]] = []
for m in masks_list:
    new_rles.extend(m._rles)

new_crop_shapes: npt.NDArray[np.int32] = np.concatenate(
    [m._crop_shapes for m in masks_list], axis=0
)
new_offsets: npt.NDArray[np.int32] = np.concatenate(
    [m._offsets for m in masks_list], axis=0
)

list.extend copies N reference pointers. np.concatenate on (N, 2) int32 arrays copies N x 8 bytes per array.

At FHD-200-50%-v600, dense merge takes 29.71 ms; compact takes 0.03 ms — a 929x speedup. At SAT-200-20%-v128 the speedup reaches 89 046x.

Note: Detections.merge calls is_empty() on each input. Before the len(xyxy) > 0 short-circuit was added, is_empty() invoked __eq__ which called np.array_equal(self.to_dense(), ...) — materialising the entire (N, H, W) CompactMask to dense just to check emptiness. The fix:

# detection/core.py — Detections.is_empty (fixed)
if len(self.xyxy) > 0:
    return False

This O(1) check avoids the O(N x H x W) dense materialisation that previously dominated compact merge time.

Dense move_masks: allocates a new (N, new_H, new_W) array and copies each mask with shifted slice coordinates — O(N x H x W):

# detection/utils/masks.py — move_masks
mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
# ... source/destination slicing logic ...
mask_array[:, dst_y1:dst_y2, dst_x1:dst_x2] = masks[:, src_y1:src_y2, src_x1:src_x2]

Compact with_offset(dx, dy): vectorised bounds check first. All new bounding-box positions are computed in a single numpy op. When none overflow the new canvas — the common case in InferenceSlicer — the RLE data is not touched at all:

# detection/compact_mask.py — CompactMask.with_offset (fast path)
new_offsets = self._offsets + np.array([dx, dy], dtype=np.int32)  # O(N) numpy
needs_clip = (x1s < 0) | (y1s < 0) | (x2s >= new_w) | (y2s >= new_h)
if not needs_clip.any():
    return CompactMask(
        list(self._rles), self._crop_shapes.copy(), new_offsets, new_image_shape
    )

When a crop does overflow (e.g. object at a tile edge), only that crop is decoded, sliced, and re-encoded. Masks fully outside bounds get a 1x1 all-False stub without any decoding.

At FHD-200-50%-v600, dense offset takes 42.30 ms; compact takes 0.02 ms — a 2 016x speedup. At SAT-200-20%-v128 the speedup reaches 290 779x.

In the InferenceSlicer pipeline the canvas is always expanded by the tile offset, so no crop ever overflows — the fast path is always taken. Clipping only activates for objects that genuinely straddle the image boundary.

Dense: np.tensordot reads every pixel of every mask to compute weighted coordinate sums:

# detection/utils/masks.py — dense centroid path
vertical_indices, horizontal_indices = np.indices((height, width)) + 0.5
# np.tensordot(masks, indices, axes=([1, 2], [0, 1]))
# reads all N x H x W values

Compact: per-crop loop decodes only the bounding-box region and computes centroids within that crop:

# detection/utils/masks.py — compact centroid path
crop = masks.crop(i)
crop_h, crop_w = crop.shape
x1 = int(masks.offsets[i, 0])
y1 = int(masks.offsets[i, 1])
# ...
crop_rows, crop_cols = np.indices((crop_h, crop_w))
cx = float(np.sum((crop_cols + 0.5)[crop])) / total + x1
cy = float(np.sum((crop_rows + 0.5)[crop])) / total + y1

At FHD-200-50%-v600, dense centroids takes 1 133.68 ms; compact takes 60.39 ms — a 13x speedup. At SAT-200-20%-v128 the speedup reaches 857x because the dense path must allocate and scan a 13.4 GB array.

Measured speedups at the FHD-200-50%-v600 operating point (dense fill, complex polygons — a realistic hard case). Dense baseline = 1x.

All speedups are larger at sparser fill fractions and larger resolutions. At SAT-200-20%-v128, .area reaches 1 204x and merge reaches 89 046x. At the sparsest scenarios (5% fill, 8-vertex polygons), memory ratios exceed 60 000x.

CompactMask implements the same duck-typed interface as np.ndarray:

import supervision as sv
from supervision.detection.compact_mask import CompactMask

# Build from an existing dense (N, H, W) bool array:
compact = CompactMask.from_dense(masks_dense, xyxy, image_shape=(H, W))

# Use exactly like a dense mask — no other code changes needed:
detections = sv.Detections(xyxy=xyxy, mask=compact, class_id=class_ids)

# Filtering, merging, area — all work transparently:
filtered = detections[confidence > 0.5]
areas = detections.area  # RLE sum, no materialisation
merged = sv.Detections.merge([det_a, det_b])

# MaskAnnotator works without any change:
annotated = sv.MaskAnnotator().annotate(frame, detections)

# Materialise back to dense when you need raw numpy:
dense_again = compact.to_dense()  # (N, H, W) bool

Supported indexing patterns:

Run on any machine — no GPU or real model required:

uv run python examples/compact_mask/benchmark.py

Six image tiers x three fill fractions (5 / 20 / 50 %) x three vertex counts (8 / 128 / 600):

Dense timing is skipped automatically when the dense IoU/NMS array would exceed 1 GB (IOU_DENSE_SKIP_GB), preventing swap thrashing. All dense ops are skipped above 16 GB (DENSE_SKIP_GB); no scenario in the current matrix reaches that threshold. Memory is always reported as theoretical NxHxW bytes.

• Compact theor. — sum of internal numpy buffer nbytes
• Mem x — dense / compact theoretical ratio
• Area x / Filter x / Annot x / IoU x / NMS x / Merge x / Offset x / Centroids x — compact speedup over dense for each operation
• † — dense IoU+NMS skipped (dense array > 1 GB); compact still runs and is timed
• — — not shown; full per-scenario tables are printed by the benchmark script

All non-skipped scenarios pass: pixel-perfect annotation, exact area, lossless to_dense() roundtrip.

• Aerial / satellite imagery — thousands of small objects on large tiles; dense masks exhaust RAM before inference completes.
• High-density crowd / cell segmentation — N > 500 on FHD already requires several GB of mask storage per batch.
• Real-time annotation pipelines — crop-paint cuts annotation from seconds to milliseconds at 4K resolution.
• Long-running tracking — accumulated Detections across many frames stay in kilobytes rather than gigabytes.
• InferenceSlicer — with_offset() adjusts crop origins directly when stitching tile results; no dense materialisation needed.

• CompactMask is not a full np.ndarray. Call .to_dense() before passing to code that requires arbitrary ndarray methods (astype, reshape, ravel, any, all, …).
• RLE format is column-major (F-order), crop-scoped — pixel-scan order matches COCO / pycocotools, but crop scope differs from full-image scope. Use .to_dense() to materialize a full-image dense mask, then encode that mask to COCO RLE before passing it to pycocotools.
• from_dense() requires the input (N, H, W) array to fit in memory. For truly OOM-scale data, build CompactMask per-detection directly from model output crops rather than from a pre-allocated dense stack.