{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:S2XXH5O4I7YTHOVCDNZ6XP34RD","short_pith_number":"pith:S2XXH5O4","canonical_record":{"source":{"id":"2109.08203","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T20:10:12Z","cross_cats_sorted":[],"title_canon_sha256":"ae5559ca140d59814072bf42426a700cc8ba56f1876a4c081fe7e8f5a3c00700","abstract_canon_sha256":"ee6bcfe9df9ec944e1a838c9ec325bfc49f6ee51fbd64b8a7440fcb90cd206ed"},"schema_version":"1.0"},"canonical_sha256":"96af73f5dc47f133baa21b73ebbf7c88fc0442160600db46cf2f9dac33c81630","source":{"kind":"arxiv","id":"2109.08203","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08203","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08203v2","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08203","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_12","alias_value":"S2XXH5O4I7YT","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_16","alias_value":"S2XXH5O4I7YTHOVC","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_8","alias_value":"S2XXH5O4","created_at":"2026-07-05T06:09:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:S2XXH5O4I7YTHOVCDNZ6XP34RD","target":"record","payload":{"canonical_record":{"source":{"id":"2109.08203","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T20:10:12Z","cross_cats_sorted":[],"title_canon_sha256":"ae5559ca140d59814072bf42426a700cc8ba56f1876a4c081fe7e8f5a3c00700","abstract_canon_sha256":"ee6bcfe9df9ec944e1a838c9ec325bfc49f6ee51fbd64b8a7440fcb90cd206ed"},"schema_version":"1.0"},"canonical_sha256":"96af73f5dc47f133baa21b73ebbf7c88fc0442160600db46cf2f9dac33c81630","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:19.320974Z","signature_b64":"f3Fy9XQE5fse9M0UpheiQWgeKNwnI9ltzDiiL06LI2iwd/wrdfzBzFc9Cs26gAhEgo5hqpe7sx9hnB2+D8viBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96af73f5dc47f133baa21b73ebbf7c88fc0442160600db46cf2f9dac33c81630","last_reissued_at":"2026-07-05T06:09:19.320518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:19.320518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.08203","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:09:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J2tIrH4MTrd/ISYUR66Et1pyU475JpiwsNGKLn2VQ5Zf8/38Sn/YzvAOuklrsE+sqVt9/XXFO86JzGaHUj41Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:15:20.572133Z"},"content_sha256":"1142dc65e6c7fbbc2b6a000b16f53bb0dc74cfa582af50e3f19df882cdb5c253","schema_version":"1.0","event_id":"sha256:1142dc65e6c7fbbc2b6a000b16f53bb0dc74cfa582af50e3f19df882cdb5c253"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:S2XXH5O4I7YTHOVCDNZ6XP34RD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Picard","submitted_at":"2021-09-16T20:10:12Z","abstract_excerpt":"In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (up to $10^4$) on CIFAR 10 and I also scan fewer seeds on Imagenet using pre-trained models to investigate large scale datasets. The conclusions are that even if the variance is not very large, it is surprisingly easy to find an outlier that performs much better or much worse than the average."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08203","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.08203/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:09:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sNsxEa9zuCJ7RkLgT0uXZt851MHTJedFOrAiuKAxFc1r9e9xK1QlKGgPo4C0mcON9frmyX10/yuLqwwFGKAkAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:15:20.574722Z"},"content_sha256":"2afc7f7236cb61756453d6ecf6556282d8967976317612dab3c7f7fbe0ada68b","schema_version":"1.0","event_id":"sha256:2afc7f7236cb61756453d6ecf6556282d8967976317612dab3c7f7fbe0ada68b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/bundle.json","state_url":"https://pith.science/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T15:15:20Z","links":{"resolver":"https://pith.science/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD","bundle":"https://pith.science/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/bundle.json","state":"https://pith.science/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S2XXH5O4I7YTHOVCDNZ6XP34RD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:S2XXH5O4I7YTHOVCDNZ6XP34RD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ee6bcfe9df9ec944e1a838c9ec325bfc49f6ee51fbd64b8a7440fcb90cd206ed","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T20:10:12Z","title_canon_sha256":"ae5559ca140d59814072bf42426a700cc8ba56f1876a4c081fe7e8f5a3c00700"},"schema_version":"1.0","source":{"id":"2109.08203","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08203","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08203v2","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08203","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_12","alias_value":"S2XXH5O4I7YT","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_16","alias_value":"S2XXH5O4I7YTHOVC","created_at":"2026-07-05T06:09:19Z"},{"alias_kind":"pith_short_8","alias_value":"S2XXH5O4","created_at":"2026-07-05T06:09:19Z"}],"graph_snapshots":[{"event_id":"sha256:2afc7f7236cb61756453d6ecf6556282d8967976317612dab3c7f7fbe0ada68b","target":"graph","created_at":"2026-07-05T06:09:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.08203/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (up to $10^4$) on CIFAR 10 and I also scan fewer seeds on Imagenet using pre-trained models to investigate large scale datasets. The conclusions are that even if the variance is not very large, it is surprisingly easy to find an outlier that performs much better or much worse than the average.","authors_text":"David Picard","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T20:10:12Z","title":"Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08203","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1142dc65e6c7fbbc2b6a000b16f53bb0dc74cfa582af50e3f19df882cdb5c253","target":"record","created_at":"2026-07-05T06:09:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ee6bcfe9df9ec944e1a838c9ec325bfc49f6ee51fbd64b8a7440fcb90cd206ed","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T20:10:12Z","title_canon_sha256":"ae5559ca140d59814072bf42426a700cc8ba56f1876a4c081fe7e8f5a3c00700"},"schema_version":"1.0","source":{"id":"2109.08203","kind":"arxiv","version":2}},"canonical_sha256":"96af73f5dc47f133baa21b73ebbf7c88fc0442160600db46cf2f9dac33c81630","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"96af73f5dc47f133baa21b73ebbf7c88fc0442160600db46cf2f9dac33c81630","first_computed_at":"2026-07-05T06:09:19.320518Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:09:19.320518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f3Fy9XQE5fse9M0UpheiQWgeKNwnI9ltzDiiL06LI2iwd/wrdfzBzFc9Cs26gAhEgo5hqpe7sx9hnB2+D8viBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:09:19.320974Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.08203","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1142dc65e6c7fbbc2b6a000b16f53bb0dc74cfa582af50e3f19df882cdb5c253","sha256:2afc7f7236cb61756453d6ecf6556282d8967976317612dab3c7f7fbe0ada68b"],"state_sha256":"cbdf8d5ee0f7a9c414f6b7c62e8d6e557f3b109245a04c4ae5285a2eb24084d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x32MaB+QWzCwleYP7sMe8JuvaN13tBuMjk3ZOZvlaRXt/C6cxN3bLgHC9H3jl38bao1dQ/n6j2Lp3iY632naBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T15:15:20.590935Z","bundle_sha256":"8228fd26f561be6d6986b4270572d67ccdff362e413902f9a5bba81b1d5ab28b"}}