{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CPQ3SVHTB2BA2UNFDOZ3T6T7F4","short_pith_number":"pith:CPQ3SVHT","schema_version":"1.0","canonical_sha256":"13e1b954f30e820d51a51bb3b9fa7f2f14df7ca544c9e57c8eab9e962a804f8f","source":{"kind":"arxiv","id":"2509.05490","version":1},"attestation_state":"computed","paper":{"title":"An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ana M. Bernardos, Andrzej D. Dobrzycki, Jos\\'e R. Casar","submitted_at":"2025-09-05T20:39:43Z","abstract_excerpt":"The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strateg"},"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":"2509.05490","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T20:39:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e45944ebce8ce4e3f3573702812c6ecb263b756f7f75519aea209b451f81ae3a","abstract_canon_sha256":"5cf5ee9a2383d8443a131cfc59e4ca97e591bc05398ff906124dff20323caffc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:48.468102Z","signature_b64":"J8PeUUwuOgW2YWdPL/q18IGLJ+veEjrRxwRNknaR0Yg60YaahTi1yRgOkJ/Ii+fYOgNmq+df+gnb9CmXLZd+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13e1b954f30e820d51a51bb3b9fa7f2f14df7ca544c9e57c8eab9e962a804f8f","last_reissued_at":"2026-07-05T12:05:48.467658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:48.467658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ana M. Bernardos, Andrzej D. Dobrzycki, Jos\\'e R. Casar","submitted_at":"2025-09-05T20:39:43Z","abstract_excerpt":"The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strateg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05490","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/2509.05490/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":"2509.05490","created_at":"2026-07-05T12:05:48.467715+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05490v1","created_at":"2026-07-05T12:05:48.467715+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05490","created_at":"2026-07-05T12:05:48.467715+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPQ3SVHTB2BA","created_at":"2026-07-05T12:05:48.467715+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPQ3SVHTB2BA2UNF","created_at":"2026-07-05T12:05:48.467715+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPQ3SVHT","created_at":"2026-07-05T12:05:48.467715+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/CPQ3SVHTB2BA2UNFDOZ3T6T7F4","json":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4.json","graph_json":"https://pith.science/api/pith-number/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/graph.json","events_json":"https://pith.science/api/pith-number/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/events.json","paper":"https://pith.science/paper/CPQ3SVHT"},"agent_actions":{"view_html":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4","download_json":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4.json","view_paper":"https://pith.science/paper/CPQ3SVHT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05490&json=true","fetch_graph":"https://pith.science/api/pith-number/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/graph.json","fetch_events":"https://pith.science/api/pith-number/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/action/storage_attestation","attest_author":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/action/author_attestation","sign_citation":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/action/citation_signature","submit_replication":"https://pith.science/pith/CPQ3SVHTB2BA2UNFDOZ3T6T7F4/action/replication_record"}},"created_at":"2026-07-05T12:05:48.467715+00:00","updated_at":"2026-07-05T12:05:48.467715+00:00"}