{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:IVFWM3TP6UF5IHRPZV6HZ4EGSZ","short_pith_number":"pith:IVFWM3TP","canonical_record":{"source":{"id":"2106.03112","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T13:05:57Z","cross_cats_sorted":[],"title_canon_sha256":"c44db3ae932571d4b20b865098d9ae43e74c41ea94334ea6f002c7c683871729","abstract_canon_sha256":"6026cfd6501dd17faa95105d06bf9b6a8f768961a0805caadbcfba89205564f6"},"schema_version":"1.0"},"canonical_sha256":"454b666e6ff50bd41e2fcd7c7cf086966f927426ede0f1843b4217844596869c","source":{"kind":"arxiv","id":"2106.03112","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.03112","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"arxiv_version","alias_value":"2106.03112v1","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.03112","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_12","alias_value":"IVFWM3TP6UF5","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_16","alias_value":"IVFWM3TP6UF5IHRP","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_8","alias_value":"IVFWM3TP","created_at":"2026-07-05T02:46:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:IVFWM3TP6UF5IHRPZV6HZ4EGSZ","target":"record","payload":{"canonical_record":{"source":{"id":"2106.03112","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T13:05:57Z","cross_cats_sorted":[],"title_canon_sha256":"c44db3ae932571d4b20b865098d9ae43e74c41ea94334ea6f002c7c683871729","abstract_canon_sha256":"6026cfd6501dd17faa95105d06bf9b6a8f768961a0805caadbcfba89205564f6"},"schema_version":"1.0"},"canonical_sha256":"454b666e6ff50bd41e2fcd7c7cf086966f927426ede0f1843b4217844596869c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:33.942859Z","signature_b64":"2OoAcd0DJHBUVMpKtYAeP7creqW1chz26E5bJg/VrzJnqO0yFaFWCy/zP6vJOaxKvrQLZ8/Ca01A8KbpdLZ6DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"454b666e6ff50bd41e2fcd7c7cf086966f927426ede0f1843b4217844596869c","last_reissued_at":"2026-07-05T02:46:33.942461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:33.942461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.03112","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:46:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SF1mYN2NpuskWqQ4mVaakWyyrzXgO5cIJQ1jW7GkWC4kfj9rZihhOs72KsZp0zRoxROvSSr5LM1eqh3riSTBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:04:03.219631Z"},"content_sha256":"cb68a96cd470d1f555168da024a2f09449b2a9c0f1290e943a1de5cceffac106","schema_version":"1.0","event_id":"sha256:cb68a96cd470d1f555168da024a2f09449b2a9c0f1290e943a1de5cceffac106"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:IVFWM3TP6UF5IHRPZV6HZ4EGSZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Training from Scratch for Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hong Zhang, Yang Li, Yu Zhang","submitted_at":"2021-06-06T13:05:57Z","abstract_excerpt":"The ImageNet pre-training initialization is the de-facto standard for object detection. He et al. found it is possible to train detector from scratch(random initialization) while needing a longer training schedule with proper normalization technique. In this paper, we explore to directly pre-training on target dataset for object detection. Under this situation, we discover that the widely adopted large resizing strategy e.g. resize image to (1333, 800) is important for fine-tuning but it's not necessary for pre-training. Specifically, we propose a new training pipeline for object detection tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.03112","kind":"arxiv","version":1},"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/2106.03112/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":"2026-07-05T02:46:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VZSq1+l4lX1yqWLYCfzIE3sBaKp+pKTVM5+3b0CRS7BMxt6K4K20gX23VTTOg7QmgVf1hz6HPHHY/oDDMGuZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:04:03.220147Z"},"content_sha256":"fb6e2e21bbcddb9df829d88561675f1c71041e3d48ab4d7af8236c0978752785","schema_version":"1.0","event_id":"sha256:fb6e2e21bbcddb9df829d88561675f1c71041e3d48ab4d7af8236c0978752785"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/bundle.json","state_url":"https://pith.science/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/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":"2026-08-05T16:04:03Z","links":{"resolver":"https://pith.science/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ","bundle":"https://pith.science/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/bundle.json","state":"https://pith.science/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IVFWM3TP6UF5IHRPZV6HZ4EGSZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:IVFWM3TP6UF5IHRPZV6HZ4EGSZ","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":"6026cfd6501dd17faa95105d06bf9b6a8f768961a0805caadbcfba89205564f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T13:05:57Z","title_canon_sha256":"c44db3ae932571d4b20b865098d9ae43e74c41ea94334ea6f002c7c683871729"},"schema_version":"1.0","source":{"id":"2106.03112","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.03112","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"arxiv_version","alias_value":"2106.03112v1","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.03112","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_12","alias_value":"IVFWM3TP6UF5","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_16","alias_value":"IVFWM3TP6UF5IHRP","created_at":"2026-07-05T02:46:33Z"},{"alias_kind":"pith_short_8","alias_value":"IVFWM3TP","created_at":"2026-07-05T02:46:33Z"}],"graph_snapshots":[{"event_id":"sha256:fb6e2e21bbcddb9df829d88561675f1c71041e3d48ab4d7af8236c0978752785","target":"graph","created_at":"2026-07-05T02:46:33Z","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/2106.03112/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The ImageNet pre-training initialization is the de-facto standard for object detection. He et al. found it is possible to train detector from scratch(random initialization) while needing a longer training schedule with proper normalization technique. In this paper, we explore to directly pre-training on target dataset for object detection. Under this situation, we discover that the widely adopted large resizing strategy e.g. resize image to (1333, 800) is important for fine-tuning but it's not necessary for pre-training. Specifically, we propose a new training pipeline for object detection tha","authors_text":"Hong Zhang, Yang Li, Yu Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T13:05:57Z","title":"Rethinking Training from Scratch for Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.03112","kind":"arxiv","version":1},"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:cb68a96cd470d1f555168da024a2f09449b2a9c0f1290e943a1de5cceffac106","target":"record","created_at":"2026-07-05T02:46:33Z","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":"6026cfd6501dd17faa95105d06bf9b6a8f768961a0805caadbcfba89205564f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-06T13:05:57Z","title_canon_sha256":"c44db3ae932571d4b20b865098d9ae43e74c41ea94334ea6f002c7c683871729"},"schema_version":"1.0","source":{"id":"2106.03112","kind":"arxiv","version":1}},"canonical_sha256":"454b666e6ff50bd41e2fcd7c7cf086966f927426ede0f1843b4217844596869c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"454b666e6ff50bd41e2fcd7c7cf086966f927426ede0f1843b4217844596869c","first_computed_at":"2026-07-05T02:46:33.942461Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:46:33.942461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2OoAcd0DJHBUVMpKtYAeP7creqW1chz26E5bJg/VrzJnqO0yFaFWCy/zP6vJOaxKvrQLZ8/Ca01A8KbpdLZ6DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:46:33.942859Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.03112","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb68a96cd470d1f555168da024a2f09449b2a9c0f1290e943a1de5cceffac106","sha256:fb6e2e21bbcddb9df829d88561675f1c71041e3d48ab4d7af8236c0978752785"],"state_sha256":"f2c39ead29260d98d6cfb525f26270838bf950ea2bb19aa06518ed7a5dc74238"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B4o0y758ppO3+R1Lg6jeI84lnOE8PDpUmsnEusTpt8YytWtLhhN4wA8yS34BPz1PaAKqjDGPTLOmRd2+fuNODQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:04:03.225796Z","bundle_sha256":"7f6565f520c798b967df49697db34498c6fdaf8b2ba0b01e0490437e8139fc5d"}}