{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KFTUO2YQU7YRV67H3CCIH6DSFK","short_pith_number":"pith:KFTUO2YQ","schema_version":"1.0","canonical_sha256":"5167476b10a7f11afbe7d88483f8722a9e85ddfa572d08126711d87f24872994","source":{"kind":"arxiv","id":"2207.02696","version":1},"attestation_state":"computed","paper":{"title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao","submitted_at":"2022-07-06T14:01:58Z","abstract_excerpt":"YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv"},"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":"2207.02696","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-06T14:01:58Z","cross_cats_sorted":[],"title_canon_sha256":"c8559a28d0b2cef5ba1fbc852163e59894d6529e88005620a8b509bc4fdcc7f5","abstract_canon_sha256":"a9dc277d4cb3e3f42c16e219d672640e46fa8e738156fa6010aff97db00a3e52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:06.907958Z","signature_b64":"Dxe0cZAc+iKsSNV91aNVtVm3FUn9jQ0SNVPM/LpGfAQ5r6oJkP7n9wAvpcGmJUTF6fNA2HSfTS8eqZgRQqaGAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5167476b10a7f11afbe7d88483f8722a9e85ddfa572d08126711d87f24872994","last_reissued_at":"2026-07-05T04:38:06.907499Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:06.907499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao","submitted_at":"2022-07-06T14:01:58Z","abstract_excerpt":"YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.02696","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/2207.02696/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":"2207.02696","created_at":"2026-07-05T04:38:06.907557+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.02696v1","created_at":"2026-07-05T04:38:06.907557+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.02696","created_at":"2026-07-05T04:38:06.907557+00:00"},{"alias_kind":"pith_short_12","alias_value":"KFTUO2YQU7YR","created_at":"2026-07-05T04:38:06.907557+00:00"},{"alias_kind":"pith_short_16","alias_value":"KFTUO2YQU7YRV67H","created_at":"2026-07-05T04:38:06.907557+00:00"},{"alias_kind":"pith_short_8","alias_value":"KFTUO2YQ","created_at":"2026-07-05T04:38:06.907557+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08014","citing_title":"FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2606.07965","citing_title":"Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04345","citing_title":"HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27831","citing_title":"Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2406.17323","citing_title":"XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18310","citing_title":"Comparative blobs and holes dynamics in a tokamak plasma: deep learning analysis of fast imaging data","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2602.04583","citing_title":"PEPR: Privileged Event-based Predictive Regularization for Domain Generalization","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20030","citing_title":"Learning to count small and clustered objects with application to bacterial colonies","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20000","citing_title":"RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07831","citing_title":"Explainable Part-Based Vehicle Classifier with Spatial Awareness","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK","json":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK.json","graph_json":"https://pith.science/api/pith-number/KFTUO2YQU7YRV67H3CCIH6DSFK/graph.json","events_json":"https://pith.science/api/pith-number/KFTUO2YQU7YRV67H3CCIH6DSFK/events.json","paper":"https://pith.science/paper/KFTUO2YQ"},"agent_actions":{"view_html":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK","download_json":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK.json","view_paper":"https://pith.science/paper/KFTUO2YQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.02696&json=true","fetch_graph":"https://pith.science/api/pith-number/KFTUO2YQU7YRV67H3CCIH6DSFK/graph.json","fetch_events":"https://pith.science/api/pith-number/KFTUO2YQU7YRV67H3CCIH6DSFK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK/action/storage_attestation","attest_author":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK/action/author_attestation","sign_citation":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK/action/citation_signature","submit_replication":"https://pith.science/pith/KFTUO2YQU7YRV67H3CCIH6DSFK/action/replication_record"}},"created_at":"2026-07-05T04:38:06.907557+00:00","updated_at":"2026-07-05T04:38:06.907557+00:00"}