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Missing-region (centre-circle) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (centre-rect) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (corner) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (edge-crop) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (irregular-tear) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (transparent-hole) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Missing-region (vertical-strip) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
White = missing region for the centre-circle cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the centre-rect cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the corner cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the edge-crop cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the irregular-tear cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the transparent-hole cut. Feed this mask with the cut input to an inpainting tool.
White = missing region for the vertical-strip cut. Feed this mask with the cut input to an inpainting tool.
Twenty input types crossed with the seven attributes that may or may not apply to them, so a form builder can be checked for attributes it emits on controls that ignore them.
One control per inputmode token - none, text, decimal, numeric, tel, search, email and url - each paired with the input type it realistically accompanies, for testing mobile keyboard selection and hint propagation.
An instruction-tuning dataset in the Alpaca JSONL format: 20 instruction / input / output triples covering small transformations, extraction, and factual answers. Fully synthetic; a fixture for supervised fine-tuning pipelines.
Fillable fictional insurance claim PDF for claims-form parser and filler tests.
Policy number, loss date, description, and photo upload for insurance-claim form tooling tests.
Six large integers written both as JSON numbers and as strings, including the 2^53 boundary where consecutive integers stop being distinguishable in a double. A parser backed by doubles turns 9007199254740993 into 9007199254740992 and reports no error at all.
An intentionally corrupt DICOM-shaped file whose header, meta group and every non-pixel element are valid, and whose PixelData element declares a length the file does not contain. A viewer must report the short read rather than render a partial image padded with whatever it found.
An intentionally corrupt FASTQ whose first five records are complete and whose sixth ends after the plus line, leaving no quality string. A four-line-block reader must report an incomplete final record rather than pairing the sequence with an empty quality string.
An intentionally corrupt FITS file whose header is complete and whose data unit is cut short, leaving a total length that is not a multiple of the mandatory 2880-byte block. A reader must report the short read rather than hand back a half-filled array padded with whatever it found.
An intentionally corrupt HDF5 file cut to 1024 bytes, so the 8-byte signature and superblock survive while the object headers and B-trees they point at do not. Format sniffing will say HDF5 and opening must fail loudly, which is exactly the split this fixture measures.
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