{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:AESU3BWBUS24DCHLZBVYNVFZG4","short_pith_number":"pith:AESU3BWB","schema_version":"1.0","canonical_sha256":"01254d86c1a4b5c188ebc86b86d4b937014fa002b0fd71be4b24a9d25bc9afd4","source":{"kind":"arxiv","id":"2007.09336","version":1},"attestation_state":"computed","paper":{"title":"AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Xu, Tingzhong Tian, Wenshuo Ma, Yimin Huang, Zhenguo Li","submitted_at":"2020-07-18T05:44:26Z","abstract_excerpt":"Most state-of-the-art object detection systems follow an anchor-based diagram. Anchor boxes are densely proposed over the images and the network is trained to predict the boxes position offset as well as the classification confidence. Existing systems pre-define anchor box shapes and sizes and ad-hoc heuristic adjustments are used to define the anchor configurations. However, this might be sub-optimal or even wrong when a new dataset or a new model is adopted. In this paper, we study the problem of automatically optimizing anchor boxes for object detection. We first demonstrate that the number"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2007.09336","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-18T05:44:26Z","cross_cats_sorted":[],"title_canon_sha256":"fcc066054ce568665c4e73983d31a8902a6fcc356f7f946b6857a1a1ee0815be","abstract_canon_sha256":"53d20c9adf546816787040021bda55b1454817ab2d6123f5e1811898075a3fdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:20:23.509823Z","signature_b64":"/JdJmtI3YegnyOh5bUsC8aT9xarQ3ew0cEY7TNj1nQZld0ZXdbJN3/xathf0cFZHeR9FTkTxPzcSDy6cxwUVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01254d86c1a4b5c188ebc86b86d4b937014fa002b0fd71be4b24a9d25bc9afd4","last_reissued_at":"2026-07-05T01:20:23.509335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:20:23.509335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Xu, Tingzhong Tian, Wenshuo Ma, Yimin Huang, Zhenguo Li","submitted_at":"2020-07-18T05:44:26Z","abstract_excerpt":"Most state-of-the-art object detection systems follow an anchor-based diagram. Anchor boxes are densely proposed over the images and the network is trained to predict the boxes position offset as well as the classification confidence. Existing systems pre-define anchor box shapes and sizes and ad-hoc heuristic adjustments are used to define the anchor configurations. However, this might be sub-optimal or even wrong when a new dataset or a new model is adopted. In this paper, we study the problem of automatically optimizing anchor boxes for object detection. We first demonstrate that the number"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.09336","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/2007.09336/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2007.09336","created_at":"2026-07-05T01:20:23.509389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.09336v1","created_at":"2026-07-05T01:20:23.509389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.09336","created_at":"2026-07-05T01:20:23.509389+00:00"},{"alias_kind":"pith_short_12","alias_value":"AESU3BWBUS24","created_at":"2026-07-05T01:20:23.509389+00:00"},{"alias_kind":"pith_short_16","alias_value":"AESU3BWBUS24DCHL","created_at":"2026-07-05T01:20:23.509389+00:00"},{"alias_kind":"pith_short_8","alias_value":"AESU3BWB","created_at":"2026-07-05T01:20:23.509389+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4","json":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4.json","graph_json":"https://pith.science/api/pith-number/AESU3BWBUS24DCHLZBVYNVFZG4/graph.json","events_json":"https://pith.science/api/pith-number/AESU3BWBUS24DCHLZBVYNVFZG4/events.json","paper":"https://pith.science/paper/AESU3BWB"},"agent_actions":{"view_html":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4","download_json":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4.json","view_paper":"https://pith.science/paper/AESU3BWB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.09336&json=true","fetch_graph":"https://pith.science/api/pith-number/AESU3BWBUS24DCHLZBVYNVFZG4/graph.json","fetch_events":"https://pith.science/api/pith-number/AESU3BWBUS24DCHLZBVYNVFZG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4/action/storage_attestation","attest_author":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4/action/author_attestation","sign_citation":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4/action/citation_signature","submit_replication":"https://pith.science/pith/AESU3BWBUS24DCHLZBVYNVFZG4/action/replication_record"}},"created_at":"2026-07-05T01:20:23.509389+00:00","updated_at":"2026-07-05T01:20:23.509389+00:00"}