ErrLookup › roboflow/supervision

roboflow/supervision

We write your reusable computer vision tools. 💜 · Python · 98 source files

Analyzed at 7f254d9784 on 2026-08-15. 391 documented errors.

Code / MessageTypeSeverityTags
module {__name__} has no attribute {name}
exception error validation, color, hex, valueerror
Edge indices must use the 1-based convention and be within t
validation error validation, color, range, valueerror
sigma must contain at least one value
validation error validation, color, rgb, range, valueerror
All sigma values must be positive
validation error validation, color, bgr, opencv, range, valueerror
max_axis must be positive when provided
validation error validation, color, rgba, alpha, range, valueerror
color length ({len(color_seq)}) must match sigma length ({le
validation error validation, color, bgra, alpha, opencv, range, valueerror
key_points.data must contain 'covariance' with shape (N, K,
validation error validation, color-palette, matplotlib, valueerror
Expected covariance shape {expected_shape}, got {covariances
validation error validation, color-palette, empty-list, valueerror
Number of labels ({len(resolved)}) must match number of key
validation error ci, packaging, wheel, opencv, valueerror
labels is a dict but class_id is None; KeyPoints must have c
validation error ci, packaging, wheel, opencv, extras, valueerror
No labels defined for class_id={class_id}.
validation error ci, packaging, manifest, drift, valueerror
Number of colors ({len(colors)}) must match number of key po
validation error ci, packaging, wheel, metadata, malformed, valueerror
edges is a dict but class_id is None; KeyPoints must have cl
validation error metrics, precision, class-id, input-validation
No edges defined for class_id={class_id}.
validation error metrics, precision, confidence, input-validation
from_inference() operates on a single result at a time.You c
validation error metrics, precision, metric-target, defensive-code
2D boolean mask row count {mask.shape[0]} does not match obj
validation error metrics, f1-score, metric-target, defensive-code
2D boolean mask column count {mask.shape[1]} does not match
validation error metrics, f1-score, metric-target, defensive-code
Cannot filter keypoints with a 2D boolean mask where rows ha
validation error metrics, f1-score, input-validation, batch-evaluation
Value must be a np.ndarray or a list
validation error metrics, f1-score, shape-validation, internal-api
All KeyPoints must have the same number of keypoints per ske
validation error metrics, f1-score, masks, segmentation
All KeyPoints must have the same coordinate depth per skelet
validation error keypoints, pose-estimation, merge, validation
KeyPoints detection_confidence must be given for NMS to be e
validation error keypoints, nms, confidence, validation
KeyPoints class_id must be given for NMS to be executed. If
validation error keypoints, nms, class-id, validation
Cannot pass both 'confidence' and 'keypoint_confidence'. 'co
validation error keypoints, deprecation, api-migration, validation
All or none of the '{name}' fields must be None
validation error keypoints, merge, validation, optional-fields
Unsupported MediaPipe result type. Expected an object with p
validation error keypoints, mediapipe, connector, api-mismatch
Downloaded asset {filename} failed MD5 verification.
validation error assets, download, md5, network, integrity
Invalid asset. It should be one of the following: {valid_ass
validation error assets, download, invalid-argument, enum
Detections must have class_id attribute.
validation error dataset, class-id, detections, validation
Detections class_id must be a subset of source_to_target_map
validation error dataset, class-id, mapping, validation
Class {class_name} not found in target classes. source_class
validation error dataset, class-mapping, merge, validation
Cannot export dataset: image paths {first_path} and {image_p
validation error dataset, export, filenames, validation
The keys of the images and annotations dictionaries must mat
validation error dataset, constructor, key-mismatch, validation
Could not read image from path: {image_path}
validation error dataset, opencv, image-io, path
Merging lazy and in-memory DetectionDatasets is not supporte
validation error dataset, merge, lazy-loading, api-migration
Image paths {duplicates} are not unique across datasets.
validation error dataset, merge, duplicates, validation
Detection annotation for image {image_path} contains non-int
validation error dataset, class-id, dtype, coco, validation
Detection annotation for image {image_path} contains class_i
validation error dataset, class-id, out-of-range, coco, validation
starting_image_id and starting_annotation_id must be >= 1 (C
validation error coco, dataset-export, validation, argument-validation
Detections must include class_id for COCO export.
validation error coco, detections, class-id, dataset-export
COCO annotation refers to image {image_name}, which resolves
validation error coco, dataset-load, path-validation, data-quality
COCO annotation refers to image {image_name}, which resolves
validation error coco, dataset-load, path-traversal, security
COCO annotation refers to image {image_name}, which resolves
validation error coco, dataset-load, path-validation, data-quality
COCO annotation file contains duplicate entries for image {i
validation error coco, dataset-load, duplicates, data-quality
COCO annotation refers to image {image_name}, which produces
validation error coco, dataset-load, path-validation, corruption
CreateML annotation refers to image {image_name}, which reso
validation error createml, dataset-load, path-validation, data-quality
CreateML annotation refers to image {image_name}, which reso
validation error createml, dataset-load, path-traversal, security
CreateML annotation refers to image {image_name}, which reso
validation error createml, dataset-load, path-validation, data-quality
CreateML annotation file must contain a JSON list at the roo
validation error createml, dataset-load, json, format-mismatch
class_id is required for CreateML export, but the provided D
validation error createml, detections, class-id, dataset-export
CreateML annotation refers to image {image_name}, which prod
validation error createml, dataset-load, path-validation, corruption
Malformed CreateML annotation entry (missing or non-string '
validation error createml, dataset-load, schema, data-quality
CreateML annotation entry is missing the required 'image' ke
validation error createml, dataset-load, schema, missing-key
CreateML annotation file contains duplicate entries for imag
validation error createml, dataset-load, duplicates, data-quality
Malformed CreateML annotation entry {annotation}: {exc}
validation error createml, dataset-load, schema, coordinates
Missing '{tag}' in Pascal VOC annotation.
validation error pascal-voc, xml, dataset-load, schema
Detections must include class_id for Pascal VOC export.
validation error pascal-voc, detections, class-id, dataset-export
Detections class_id must be an integer for Pascal VOC export
validation error pascal-voc, dtype, class-id, dataset-export
Failed to parse XML root from {annotation_path}
validation error pascal-voc, dataset, xml, parsing
Could not read image from path: {image_path}
validation error pascal-voc, dataset, opencv, image-io
Missing bndbox in Pascal VOC annotation.
validation error pascal-voc, dataset, xml, annotation-format
Missing polygon coordinate value in Pascal VOC.
validation error pascal-voc, dataset, polygon, xml
Expected 'names' to be a list or dict in data.yaml at '{file
validation error yolo, dataset, yaml, config
Expected mapping in data.yaml at '{file_path}', got {type(da
validation error yolo, dataset, yaml, config
`is_obb=True` requires `'{ORIENTED_BOX_COORDINATES}'` in `de
validation error yolo, obb, export, detections
Expected 'names' dict in data.yaml at '{file_path}' to have
validation error yolo, dataset, yaml, config
Class ID is required for YOLO annotations.
validation error yolo, export, detections, class-id
Detections class_id must be an integer for YOLO export, got
validation error yolo, export, detections, dtype
OBB data for each detection must have shape (4, 2), got {cor
validation error yolo, obb, export, shape
class_id is required for LabelMe export, but the provided De
validation error labelme, export, detections, class-id
LabelMe shape of type {shape_type} is missing the required {
validation error labelme, dataset, json, annotation-format
LabelMe shape of type {shape_type} (label={label}) has malfo
validation error labelme, dataset, json, shape
A LabelMe annotation file is missing the required 'imagePath
validation error labelme, dataset, json, annotation-format
LabelMe annotation has an invalid 'imagePath' {raw_image_pat
validation error labelme, dataset, path-traversal, json
Duplicate image basename {image_name} resolved from multiple
validation error labelme, dataset, duplicate, annotation-format
LabelMe annotation for {image_name} requires 'imageWidth' an
validation error labelme, dataset, masks, json
class_id {class_index} at detection index {index} is out of
validation error labelme, export, class-id, validation
LabelMe rectangle shape (label={label}) has {len(points)} po
validation error labelme, dataset, rectangle, json
LabelMe polygon shape (label={label}) has {len(points)} poin
validation error labelme, dataset, annotation, polygon, validation
COCO RLE counts must be one-dimensional.
validation error coco, rle, compact-mask, segmentation, validation
COCO RLE counts cannot be empty.
validation error coco, rle, compact-mask, empty-input, validation
COCO RLE counts must be non-negative.
validation error coco, rle, compact-mask, corrupt-data, validation
Invalid COCO RLE counts.
validation error coco, rle, compact-mask, type-error, validation
image_shape must contain positive height and width.
validation error compact-mask, coco, rle, image-shape, validation
image_shape {(img_h, img_w)} exceeds the maximum allowed dim
validation error compact-mask, coco, rle, limits, image-shape
xyxy must have shape (N, 4), where N matches the number of R
validation error compact-mask, coco, rle, bounding-box, shape-mismatch
Cannot merge an empty list of CompactMask objects.
validation error compact-mask, merge, empty-input, slicing
new_image_shape must contain positive dimensions
validation error compact-mask, offset, image-shape, slicing, validation
COCO RLE counts exceed int32 range.
validation error coco, rle, compact-mask, overflow, int32
Each RLE payload must be a mapping.
validation error coco, rle, compact-mask, type-error, api-contract
Each RLE payload must contain 'size' and 'counts'.
validation error coco, rle, compact-mask, missing-key, api-contract
RLE size {(rle_h, rle_w)} must match image_shape {(img_h, im
validation error coco, rle, compact-mask, resolution-mismatch, image-shape
The sum of COCO RLE counts must match the image area.
validation error coco, rle, compact-mask, corrupt-data, validation
Cannot merge CompactMask objects with different image shapes
validation error compact-mask, merge, image-shape, resolution-mismatch
RLE size must be [height, width].
validation error coco, rle, compact-mask, size-format, type-error
Unsupported VLM value: {vlm}.
validation error vlm, detections, enum, api-contract, dispatch
{anchor} is not supported.
validation error detections, anchor, position, enum, api-contract
Both Detections should have exactly 1 detected object.
validation error detections, merge, validation, supervision
The provided Transformers results do not contain any valid f
validation error transformers, huggingface, from-transformers, validation
Azure API returned an error {azure_result['error']['message'
validation error azure, api, auth, network, vision
EasyOCR results produced with detail=0 do not include boundi
validation error easyocr, ocr, detail, from-easyocr
EasyOCR results must contain four corner points per detectio
validation error easyocr, ocr, shape, numpy, from-easyocr
Value must be a np.ndarray or a list
validation error set-data, type, detections, metadata
Detections class_id must be given for {operation_name} to be
validation error nms, class-id, class-agnostic, detections
Detections confidence must be given for NMS to be executed.
validation error nms, confidence, detections, suppression
Detections confidence must be given for Soft-NMS to be execu
validation error soft-nms, confidence, detections, suppression
Detections confidence must be given for NMM to be executed.
validation error nmm, merge, confidence, detections
Field '{attribute}' should be consistently None or not None
validation error merge, field-mismatch, detections, validation
SAM segmentations must all be dense arrays or COCO RLE dicti
validation error sam, segmentation, rle, type, from-sam
Invalid type for 'lmm': {type(lmm)}. Must be LMM or str.
validation error vlm, lmm, dispatch, type, deprecated
Invalid VLM result type: {type(result)}. Must be str.
validation error vlm, paligemma, type, from-vlm, decode
Invalid VLM result type: {type(result)}. Must be dict.
validation error vlm, florence-2, type, from-vlm, post-process
All or none of the 'mask' fields must be None
validation error
Cannot merge CompactMask objects with different image shapes
validation error
All or none of the '{name}' fields must be None
validation error
corners must have shape (N, 4, 2); got {c.shape}
validation error
Cannot merge CompactMask objects with different image shapes
validation error
Cannot use `Position.CENTER_OF_MASS` without a detection mas
validation error
Invalid LMM string '{lmm}'. Must be one of {[m.value for m i
validation error
Dense mask shape {dense.shape[1:]} does not match CompactMas
validation error
Unimplemented task: {task}
exception error
Invalid value type: {type(value)}. Must be an instance of {c
validation error
Invalid VLM result type: {type(result)}. Must be {RESULT_TYP
validation error
Number of ref tags ({len(label_segments)}) and det tags ({le
validation error
Expected result with a single element. Got: {result}
validation error
{task} not supported. Supported tasks are: {SUPPORTED_TASKS_
validation error
Both dimensions in resolution_wh must be positive. Got ({w},
validation error
Missing required argument: {arg}
validation error
Argument {arg} is not allowed for {vlm.name}
validation error
Expected string as {task} result, got {type(result)}
validation error florence-2, vlm, task-mismatch, validation
Expected string to end in location tags, but got {result}
validation error florence-2, vlm, parsing, loc-tags
Invalid vlm value: {vlm}. Must be one of {[e.value for e in
validation error vlm, enum, validation, config
Invalid value: {value}. Must be one of {cls.list()}
validation warning deprecation, enum, vlm, migration
Triggering anchors cannot be empty.
validation error line-zone, validation, constructor, anchors
The magnitude of the vector cannot be zero.
validation error line-zone, geometry, validation
Invalid table position. Supported values are: TOP_LEFT, TOP_
validation error line-zone, annotator, position, validation
The number of line zones and their labels must match.
validation error line-zone, annotator, length-mismatch, validation
Incorrect connectivity value. Possible connectivity values:
validation error masks, connectivity, opencv, validation
mask must be boolean
validation error masks, dtype, typeerror, opencv
Either absolute_distance or relative_distance must be set.
validation error masks, required-argument, validation
mode must be 'edge' or 'centroid'
validation error masks, mode, validation, string-literal
the sum of the number of pixels in the RLE must be the same
validation error rle, mask, resolution-mismatch, coco
Input mask must be 2D
validation error rle, mask, shape, batch-dim
Input mask cannot be empty
validation error rle, mask, empty-input, validation
coordinate_convention must be 'inclusive' or 'exclusive', go
validation error converters, masks, convention, validation
Malformed compressed RLE string: unexpected end at position
validation error rle, decoding, data-corruption, truncation
All data dictionaries must have the same keys to merge.
validation error detections, merge, data-dict, key-mismatch
All metadata dictionaries must have the same keys to merge.
validation error detections, metadata, merge, validation
All data values within a single object must have equal lengt
validation error detections, data-dict, merge, validation
Inconsistent data types for key '{key}'. Only np.ndarray and
validation error detections, data-dict, merge, numpy
Unsupported data type for key '{key}': {type(value)}
validation error detections, data-dict, indexing, typeerror
Conflicting metadata for key: '{key}': {type(value)}, {type(
validation error detections, metadata, merge, numpy
Unexpected array dimension for key '{key}'.
validation error detections, data-dict, merge, numpy
Conflicting metadata for key: '{key}'.
validation error detections, metadata, merge, conflict
Unsupported index type: {type(index)}
validation error detections, indexing, typeerror, numpy
Invalid value type: {type(value)}. Must be an instance of {c
validation error enum, validation, nms, api-misuse
box coordinates must be real-valued
validation error iou, numpy, dtype, validation
`is_crowd` length ({len(is_crowd)}) must match `boxes_true`
validation error iou, evaluation, shape-mismatch, validation
masks_true and masks_detection must be 3D (N, H, W); got ndi
validation error masks, iou, shape-mismatch, validation
masks_true and masks_detection must share the same (H, W); g
validation error masks, iou, shape-mismatch, resolution
overlap_metric {overlap_metric} is not supported, only 'IOU'
validation error argument-validation, enum, iou, detection
`{name}` has shape {arr.shape}; expected (N, 4, 2) — each bo
validation error shape-validation, oriented-boxes, iou, numpy, detection
Empty group detected when non-max-merging detections: {merge
validation error internal-invariant, non-max-merge, nms, detection
`{name}` has shape {arr.shape}; expected (N, 8) for flat YOL
validation error shape-validation, oriented-boxes, iou, numpy, detection
`{name}` has shape {arr.shape}; expected (N, 5) or (N, 6).
validation error shape-validation, oriented-boxes, nms, numpy, detection
Invalid value: {value}. Must be one of {cls.list()}
validation error argument-validation, enum, nms, post-processing, detection
`{name}` must be 2-D (N, 8) or 3-D (N, 4, 2), got shape {arr
validation error shape-validation, oriented-boxes, iou, numpy, detection
Percentage must be in the range [0, 1).
validation error argument-validation, polygons, geometry, range-check
epsilon_step must be positive.
validation error polygons, validation, douglas-peucker, valueerror
corners must have shape (N, 4, 2); got {corners.shape}
validation error oriented-bounding-box, numpy, shape-validation, valueerror
xyxyxyxy must have shape (N, 4, 2); got {xyxyxyxy.shape}
validation error oriented-bounding-box, coordinate-conversion, numpy, shape-validation, valueerror
xyxyxyxy must have shape (N, 4, 2); got {corners.shape}
validation error oriented-bounding-box, anchors, numpy, shape-validation, internal-api, valueerror
{anchor} is not supported.
validation error anchors, position, oriented-bounding-box, enum, valueerror
Triggering anchors cannot be empty.
validation error polygon-zone, anchors, validation, valueerror
Panoptic PNG masks must have at least 3 channels.
validation error panoptic-segmentation, png, transformers, image-decoding, valueerror
Object of type {type(value).__name__} is not JSON serializab
validation error json-sink, serialization, numpy, typeerror, telemetry
`slice_wh` must be an int or a tuple of two positive integer
validation error inference-slicer, configuration, validation, valueerror
`overlap_wh` must be an int or a tuple of two non negative i
validation error inference-slicer, configuration, validation, valueerror
Resolution width and height are required for moving segmenta
validation error inference-slicer, segmentation-masks, coordinates, valueerror
`thread_workers` must be a positive integer. Received: {thre
validation error inference-slicer, threading, configuration, validation, valueerror
`batch_size` must be a positive integer. Received: {batch_si
validation error inference-slicer, batching, configuration, validation, valueerror
Callback must return `list[Detections]` when `batch_size > 1
validation error inference-slicer, batching, callback-contract, valueerror
Callback returned {len(detections_in_slices)} Detections for
validation error inference-slicer, batching, callback-contract, valueerror
Overlap values must be greater than or equal to 0. Received:
validation error inference-slicer, overlap, validation, valueerror
`overlap_wh` must be smaller than `slice_wh` in both dimensi
validation error inference-slicer, overlap, tiling, validation, valueerror
InferenceSlicer requires a projected coordinate reference sy
validation error inference-slicer, geospatial, rasterio, crs, gdal, valueerror
`slice_wh` must be a positive integer. Received: {slice_wh}
validation error inference-slicer, configuration, validation, valueerror
`slice_wh` values must be positive. Received: {slice_wh}
validation error inference-slicer, configuration, validation, valueerror
`overlap_wh` must be a non negative integer. Received: {over
validation error inference-slicer, validation, configuration, valueerror
`overlap_wh` values must be non negative. Received: {overlap
validation error inference-slicer, validation, configuration, valueerror
Cannot append to CSV: The file '{self.file_name}' is not ope
exception error csv-sink, context-manager, lifecycle, exception
xyxy must be a 2D np.ndarray with shape {expected_shape}, bu
validation error detections, validation, shape, numpy
class_id must be a 1D np.ndarray with shape {expected_shape}
validation error detections, validation, shape, numpy
confidence must be a 1D np.ndarray with shape {expected_shap
validation error detections, validation, shape, numpy
tracker_id must be a 1D np.ndarray with shape {expected_shap
validation error detections, tracking, validation, shape
xy must be a 3D np.ndarray with shape {expected_shape}, but
validation error keypoints, pose, validation, shape
visible must be a 2D np.ndarray with shape (n, m), but got s
validation error keypoints, validation, shape, mask
visible first dimension must be {n}, but got shape {actual_s
validation error keypoints, validation, shape, mask
visible second dimension must be {m}, but got shape {actual_
validation error keypoints, validation, shape, mask
resolution must be a tuple of two integers, got
validation error resolution, validation, configuration, video
Both elements in resolution must be integers.
validation error resolution, validation, type, configuration
Both dimensions in resolution must be positive. Got ({w}, {h
validation error resolution, validation, configuration, video
mask must contain {n} masks, but got {len(mask)}
validation error detections, mask, compact-mask, validation
keypoint_confidence must be a 2D np.ndarray with shape (n, m
validation error keypoints, confidence, validation, shape
keypoint_confidence first dimension must be {n}, but got sha
validation error keypoints, confidence, validation, shape
keypoint_confidence second dimension must be {m}, but got sh
validation error keypoints, confidence, validation, shape
Length of list for key '{key}' must be {n}
validation error detections, data-dict, validation, alignment
Value for key '{key}' must be a list or np.ndarray
validation error detections, data-dict, validation, type
Shape of np.ndarray for key '{key}' must be ({n},)
validation error detections, numpy, shape-mismatch, data-dict
First dimension of np.ndarray for key '{key}' must have size
validation error detections, numpy, shape-mismatch, data-dict
class_id must be 1d np.ndarray with (n, ) shape
validation error classifications, numpy, shape-mismatch, constructor
confidence must be 1d np.ndarray with (n, ) shape
validation error classifications, numpy, shape-mismatch, confidence
top_k could not be calculated, confidence is None
validation error classifications, top-k, confidence, none-check
`image` must be a numpy.ndarray or PIL.Image.Image. Received
validation error image, crop, type-guard, numpy, pillow
`image` must be a numpy.ndarray or PIL.Image.Image. Received
validation error image, resolution, type-guard, numpy, pillow
Data pointed by URL could not be decoded into image.
validation error image, url, download, decode, network
Scale factor must be positive.
validation error image, scale, validation, argument-error
opacity must be between 0.0 and 1.0
validation error image, opacity, validation, argument-error
Could not create image tiles from empty list of images.
validation error image, tiles, empty-input, validation
Could not place {len(images_cv2)} in grid with size: {grid_s
validation error image, tiles, grid, capacity
Could not aggregate images shape - provided unknown mode: {m
validation error image, tiles, enum, argument-error
NumPy image must have at least 2 dimensions (H, W, ...). Rec
validation error image, resolution, numpy, shape-mismatch
Failed to save image to path: {image_path}
exception error image, save, opencv, filesystem, io
Unsupported URL scheme {parsed_url.scheme} in {url}. Only HT
validation error url, download, validation, scheme
Invalid URL {url}: no host supplied.
validation error url, download, validation, hostname
URL authority contains a backslash
validation error url, security, ssrf, validation, windows
prepared URL is empty
validation error url, download, validation, none-check
Invalid URL {url}: {error}
validation error url, download, validation, parsing, encoding
Expected shape (H,W), (H,W,3), or (H,W,4), got {image.shape}
validation error numpy, image-processing, shape-mismatch, pillow
Unsupported image type: {type(scene)}
validation error type-error, annotators, numpy, pillow
Unsupported image type: {type(image)}
validation error type-error, image-processing, numpy, pillow
Only class instances are supported, not classes.
validation error introspection, validation, api-misuse
unreadable attribute
exception error attributes, descriptor, metaprogramming
The number of images exceeds the grid size. Please increase
validation error visualization, matplotlib, notebook, validation
Could not open video at {source_path}
exception critical video, opencv, file-io, path
Requested frames are outbound
exception error video, opencv, bounds-check
prefetch must be >= 0, got {prefetch}
validation error video, validation, configuration
Could not open video at {video_path}
exception critical video, opencv, file-io, path
Could not open video writer for {self.target_path}
exception error video, opencv, file-io, codec
write_frame requires an open VideoSink context.
exception error video, context-manager, lifecycle, api-misuse
Reader thread raised: {item}
exception error video, threading, exception-chaining, opencv
image must be uint8, got {image.dtype}. Convert with image.a
validation error gui, image-processing, dtype, numpy
Unsupported color lookup strategy: {color_lookup}
validation error annotators, enum, configuration, validation
Unsupported position: {position}
validation error annotators, enum, validation, position
Detection index {detection_idx} is out of bounds for detecti
validation error annotators, bounds-check, detections, filtering
max_line_length must be a positive integer
validation error annotators, validation, text-wrapping, configuration
The number of labels ({len(labels)}) does not match the numb
validation error annotators, validation, detections, labels
Invalid hex color format: {hex_color}
validation error color, validation, annotators
RGBA must be a 4-tuple with values between 0-255.
validation error color, validation, annotators
Length of color lookup {len(color_lookup)} does not match le
validation error annotators, color, alignment
'{CLASS_NAME_DATA_FIELD}' has {len(class_names)} entries but
validation error annotators, labels, alignment
'class_id' has {len(detections.class_id)} entries but detect
validation error annotators, labels, alignment
Could not put detections into Trace because Detections do no
validation error tracking, annotators
Invalid hex digits in {hex_color}
validation error color, validation, annotators
Could not resolve color by class because Detections do not h
validation error annotators, color, configuration
Could not resolve color by track because Detections do not h
validation error tracking, annotators, color
Error: Couldn't load the icon image from {icon_path}
exception error filesystem, annotators, icons
Unsupported position: {position}
validation error annotators, position, enum
The number of icon paths provided ({len(icon_path)}) does no
validation error annotators, icons, alignment
kernel_size must be >= 1, got {kernel_size}.
validation error annotators, blur, validation
The `tracker_id` field is missing in the provided detections
validation error tracking, annotators, trace
pixel_size must be >= 1, got {pixel_size}.
validation error annotators, pixelate, validation
roundness attribute must be float between (0, 1.0]
validation error annotators, validation, configuration
The provided detections do not contain confidence values. Pl
validation error annotators, validation, detections
custom_values must be either a numpy array or a list of floa
validation error annotators, type-error, validation
The length of custom_values must match the number of detecti
validation error annotators, alignment, validation
All values in custom_values must be between 0 and 1.
validation error annotators, validation, color-mapping, numpy
start_id must be greater than {self.NO_ID}
validation error byte-track, tracking, validation, configuration
Detections confidence must be provided for tracking.
validation error byte-track, tracking, detections, validation
Polygon must have at least one vertex.
validation error geometry, polygon, validation, numpy
Drawing points must have shape (N, 2) or (N, 1, 2)
validation error cv2-fallback, drawing, pillow, shape-validation
Only unshifted drawing coordinates are supported
validation error cv2-fallback, drawing, fixed-point, pillow
Only None hierarchy is supported by the fallback
validation error cv2-fallback, drawing, contours, hierarchy
Only RETR_TREE is supported by the fallback
validation error cv2-fallback, contours, retrieval-mode
Only CHAIN_APPROX_SIMPLE is supported by the fallback
validation error cv2-fallback, contours, approximation-method
Contour input must be a two-dimensional image
validation error cv2-fallback, contours, shape-validation, image-processing
Contour border tracing did not converge
exception critical cv2-fallback, contours, internal-invariant, runtime-error
Unsupported color conversion code: {code}
validation error cv2-fallback, color-conversion, unsupported-operation
At least one channel is required
validation error cv2-fallback, channels, merge, validation
BGR/RGB conversion requires a three-channel image
validation error cv2-fallback, color-conversion, channels, shape-validation
GRAY2BGR conversion requires a two-dimensional image
validation error cv2-fallback, color-conversion, grayscale, shape-validation
BGR2GRAY conversion requires a three-channel image
validation error cv2-fallback, color-conversion, grayscale, channels
HSV2BGR conversion requires a three-channel image
validation error cv2-fallback, color-conversion, hsv, shape-validation
Only BORDER_CONSTANT is supported by the fallback
validation error cv2-fallback, padding, border, unsupported-operation
Border sizes must be non-negative
validation error cv2-fallback, padding, validation, arithmetic-bug
addWeighted fallback only supports the default output depth;
validation error cv2-fallback, blending, dtype, unsupported-operation
addWeighted inputs must have equal shapes
validation error opencv-fallback, numpy, shape-mismatch, image-blending
Resize dimensions must be positive
validation error opencv-fallback, resize, input-validation, numpy
Unsupported interpolation mode: {interpolation}
validation error opencv-fallback, resize, interpolation, environment
Mean mask must match the image height and width
validation error opencv-fallback, mask, shape-mismatch, statistics
Unsupported flip code: {flip_code}
validation error opencv-fallback, flip, input-validation
bottomLeftOrigin is not supported by the fallback
validation error opencv-fallback, puttext, pillow, unsupported-feature
VideoWriter_fourcc requires exactly four characters
validation error opencv-fallback, video-writer, fourcc, argument-unpacking
Unsupported video codec: {code}
validation error opencv-fallback, pyav, video-codec, fourcc
PyAV video fallback only supports color (3-channel BGR) fram
exception error opencv-fallback, pyav, video-writer, grayscale, not-implemented
Video writer is not open
exception error opencv-fallback, pyav, video-writer, lifecycle, error-deferred
Video frame must have shape ({self._height}, {self._width},
validation error opencv-fallback, pyav, video-writer, shape-mismatch
Video frames must use uint8 dtype
validation error opencv-fallback, pyav, video-writer, dtype, uint8
PyAV fallback supports file paths, not webcam device indexes
exception error opencv-fallback, pyav, webcam, backend-unavailable, environment
Video source has no video stream: {source}
validation error opencv-fallback, pyav, video-capture, invalid-input, media-validation
Contours must have shape (N, 2) or (N, 1, 2)
validation error opencv-fallback, contours, shape-mismatch, numpy
epsilon must be non-negative
validation error opencv-fallback, approx-poly-dp, epsilon, input-validation
Blur kernel dimensions must be positive
validation error opencv-fallback, blur, kernel-size, input-validation
Only OpenCV's default blur border is supported
validation error opencv-fallback, blur, border-type, unsupported-feature
Connected-component input must be a two-dimensional image
validation error opencv-fallback, connected-components, dimensionality, input-validation
Only 4- and 8-connectivity are supported
validation error opencv-fallback, connected-components, connectivity, input-validation
Invalid metric target: {self._metric_target}
validation warning metrics, mean-average-recall, enum, validation, exhaustiveness-guard
The number of predictions ({len(predictions)}) and targets (
validation error metrics, mean-average-recall, validation, input-shape, api-misuse
Confusion matrix must have shape (..., 3), got {confusion_ma
validation error metrics, mean-average-recall, internal-api, numpy, shape-validation
MeanAverageRecall with `MetricTarget.MASKS` requires detecti
validation error metrics, mean-average-recall, masks, segmentation, validation
MeanAverageRecall metric requires `class_id` on both predict
validation error metrics, mean-average-recall, class-id, validation, api-misuse
MeanAverageRecall metric requires `confidence` on prediction
validation error metrics, mean-average-recall, confidence, validation, api-misuse
Unsupported metric target for IoU calculation
validation warning metrics, mean-average-recall, enum, exhaustiveness-guard, internal-api
Invalid metric target: {self._metric_target}
validation warning metrics, mean-average-precision, enum, validation, exhaustiveness-guard
results must be a list
validation error metrics, mean-average-precision, coco, validation, input-type
Results do not correspond to current coco set
validation error metrics, mean-average-precision, coco, data-alignment, validation
Evaluating predictions with caption is not supported.
exception error metrics, mean-average-precision, coco, not-implemented, captions
coco_targets must be provided
validation error metrics, mean-average-precision, coco, null-check, constructor-validation
coco_predictions must be provided
validation error metrics, mean-average-precision, coco, null-check, constructor-validation
The number of predictions ({len(predictions)}) and targets (
validation error metrics, mean-average-precision, validation, input-shape, api-misuse
The number of predictions ({total_images_predictions}) and t
validation error metrics, mean-average-precision, validation, internal-state, api-misuse
Evaluating predictions with segmentation is not supported.
exception error metrics, mean-average-precision, coco, segmentation, not-implemented
MeanAveragePrecision with `MetricTarget.MASKS` requires mask
validation error metrics, mean-average-precision, masks, segmentation, validation
MeanAveragePrecision with `MetricTarget.ORIENTED_BOUNDING_BO
validation error metrics, mean-average-precision, oriented-boxes, obb, validation
Evaluating predictions with keypoints is not supported.
exception error metrics, mean-average-precision, coco, keypoints, not-implemented
Invalid metric target: {self._metric_target}
validation error
The number of predictions ({len(predictions)}) and targets (
validation error
Confusion matrix must have shape (..., 3), got {confusion_ma
validation error
Recall with `MetricTarget.MASKS` requires detections to incl
validation error
Recall metric requires `class_id` and `confidence` on predic
validation error
Recall metric requires `class_id` on both predictions and ta
validation error
Recall metric requires `confidence` on predictions.
validation error
Unsupported metric target for IoU calculation
validation error
MetricTarget.MASKS is not currently supported for ConfusionM
validation error
{role} class ids must be finite integers.
validation error
{role} class ids must be in [0, {num_classes - 1}], got {inv
validation error
ConfusionMatrix can only be calculated for Detections with c
validation error
Number of predictions ({len(predictions)}) andtargets ({len(
validation error
Predictions must contain class_id values.
validation error
Targets must contain class_id values.
validation error
ConfusionMatrix can only be calculated for Detections with c
validation error
Predictions and targets must be lists of numpy arrays. Got {
validation error
Predictions must have shape (N, {expected_pred_cols}). Got {
validation error
Targets must have shape (N, {expected_target_cols}). Got {ta
validation error
Predictions must have shape (M, {expected_pred_cols}). Got {
validation error
Targets must have shape (N, {expected_target_cols}). Got {ta
validation error
ORIENTED_BOUNDING_BOXES requested, but {ORIENTED_BOX_COORDIN
validation error
Expected {ORIENTED_BOX_COORDINATES} to contain {len(detectio
validation error
Invalid metric target: {self._metric_target}
validation error
The number of predictions ({len(predictions)}) and targets (
validation error
Confusion matrix must have shape (..., 3), got {confusion_ma
validation error
Precision with `MetricTarget.MASKS` requires detections to i
validation error
Precision metric requires `class_id` and `confidence` on pre
validation error
Precision metric requires `class_id` on both predictions and
validation error
Precision metric requires `confidence` on predictions.
validation error
Unsupported metric target for IoU calculation
validation error
Invalid metric target: {self._metric_target}
validation error
The number of predictions ({len(predictions)}) and targets (
validation error
Confusion matrix must have shape (..., 3), got {confusion_ma
validation error
F1Score with `MetricTarget.MASKS` requires detections to inc
validation error
F1Score metric requires `class_id` and `confidence` on predi
validation error metrics, f1-score, validation, detections
F1Score metric requires `class_id` on both predictions and t
validation error metrics, f1-score, validation, class-id
F1Score metric requires `confidence` on predictions.
validation error metrics, f1-score, validation, confidence
Unsupported metric target for IoU calculation
validation error metrics, f1-score, enum, unreachable-guard
`metrics` extra is required to run the function. Run `uv pip
exception error metrics, import, optional-dependency, pandas, environment
Invalid metric type
validation error metrics, object-size, enum, validation
Bounding boxes must be shaped (N, 4)
validation error metrics, object-size, shape-validation, numpy
Areas must be shaped (N,)
validation error metrics, object-size, shape-validation, numpy
Oriented bounding boxes must be shaped (N, 4, 2)
validation error metrics, object-size, obb, shape-validation
Masks must be shaped (N, H, W)
validation error metrics, object-size, mask, shape-validation
Detection area metadata must be shaped (N,) and aligned with
validation error metrics, object-size, data-alignment, validation
Detections mask is not available
validation error metrics, object-size, mask, missing-data
Detections oriented bounding boxes are not available
validation error metrics, object-size, obb, missing-data
Image must have 3 or 4 channels.
validation error drawing, image, shape-validation, opencv
Opacity must be between 0.0 and 1.0.
validation error drawing, opacity, validation
Invalid rectangle dimensions.
validation error drawing, geometry, bounds-check, validation
Image path ('{image}') does not exist.
exception error drawing, file-io, path, file-not-found
Could not decode image path ('{image}').
exception error drawing, image-decode, opencv, file-io
Invalid characters in color hash
validation error color, validation, hex, drawing
Invalid length of color hash
validation error
Color values must be in range 0-255, got ({self.r}, {self.g}
validation error
RGB values must be in range 0-255, got ({r}, {g}, {b})
validation error
BGR values must be in range 0-255, got ({b}, {g}, {r})
validation error
RGBA values must be in range 0-255, got ({r}, {g}, {b}, {a})
validation error
BGRA values must be in range 0-255, got ({b}, {g}, {r}, {a})
validation error
color_count must be greater than or equal to 1
validation error
ColorPalette must contain at least one color
validation error
OpenCV runtime requirement remains in {wheel}: {requirements
validation error
OpenCV extra remains in {wheel}: {extras}
validation error
unexpected fallback manifest {checks}; expected {_MANIFEST_C
validation error
expected one METADATA file in {wheel}, found {metadata_paths
validation error