resize
resize: 10 implementations from 14 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 5 distinct outputs across 2 buckets, one shared input per bucket.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- Every examined implementation of this name that took an array argument ran on the shared input.
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
Bucket 1 of 2: arg 1: rank 4, kind float, dtype float32
9 implementations from 11 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 4 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 4 implementations 5 papers 227735dc8343 recorded values identical |
networks/AMT-S.py 3073f3b7 recorded x:4/float/float32; scale_factor=2.0 src/train_segmentation.py 29fed21d recorded classes:4/float/float32; size=8 sldl/video/ifrnet.py 5b17e544 recorded x:4/float/float32; scale_factor=2.0 models/SAFNet.py b44a1e73 recorded x:4/float/float32; scale_factor=2.0 |
[0.723105, 0.705598, 0.670586, 0.851331, 1.24783, 1.3592, 1.18543, 1.09854, …] shape [2, 3, 8, 8] · float32 · Tensor |
| 3 implementations 3 papers 4053d2c5338c recorded values identical |
isegm/model/is_hrnet_model.py 55bc02c6 recorded input:4/float/float32; size=(8, 10), scale_factor=None, mode='nearest', align_corners=None CatePoseEstimation/networks/SwinDRNet.py 5e6e6db8 recorded input:4/float/float32; size=(8, 10), scale_factor=None, mode='nearest', align_corners=None, warning=True models/supernet.py e8fbff5c recorded input:4/float/float32; size=(8, 10), scale_factor=None, mode='nearest', align_corners=None |
[0.723105, 0.723105, 0.723105, 0.653079, 0.653079, 1.44609, 1.44609, 1.44609, …] shape [2, 3, 8, 10] · float32 · Tensor |
| 1 implementation 2 papers 1043b9a876ff |
one code sha held from 2 papers' repositories evaluate.py 8907b630 recorded x:4/float/float32; size=(6, 10) |
[0.723105, 0.723105, 0.653079, 0.653079, 0.653079, 1.44609, 1.44609, 1.09854, …] shape [2, 3, 6, 10] · float32 · Tensor |
| 1 implementation 1 paper 41bdc3025b8b |
Mnist_attention.py c505f170 recorded images:4/float/float32; out_size=(12, 10), scale_min=0.24, scale_max=0.4 |
[0.160185, 0.146925, 0.597816, 0.506165, 1.14746, 1.06911, 0.565098, -0.334505, …] shape [2, 3, 12, 10] · float32 · Tensor |
Bucket 2 of 2: arg 1: rank 4, kind float, dtype float32 · arg 2: rank 4, kind float, dtype float32
1 implementation from 3 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 3 papers 3b830fcf50c1 |
posterior_sample.py a200f7f1 recorded y:4/float/float32, x:4/float/float32; task_name='phase_retrieval' |
[2, 2, 2, 2, 2, -2.14725, -2.54011, 2, …] shape [2, 3, 4, 4] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/resize.json.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections