{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YM4G2YPOJAXV3LZTHWUYDGKTQL","short_pith_number":"pith:YM4G2YPO","schema_version":"1.0","canonical_sha256":"c3386d61ee482f5daf333da981995382ff4f465648208cb1cb764f401e684327","source":{"kind":"arxiv","id":"1706.06782","version":2},"attestation_state":"computed","paper":{"title":"Object Detection Using Deep CNNs Trained on Synthetic Images","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hristo Bojinov, Param S. Rajpura, Ravi S. Hegde","submitted_at":"2017-06-21T08:16:29Z","abstract_excerpt":"The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications. We show that with transfer learning an effective object detector can be trained almost entirely on synthetically rendered datasets. We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic images achieves mean average precision (mAP) of 24 on a test set with 55 distinct products as objects of interest and 17 distractor objects. A further "},"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":"1706.06782","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T08:16:29Z","cross_cats_sorted":[],"title_canon_sha256":"512ceb66aa5ef15a1a7e477df876a98afde00ab2d1fafad9e309cedbc4f270df","abstract_canon_sha256":"c1da4e4d3ab50c1495aa2b14b4e10aead9e7138dba3edadc38aa2b3c2910b07a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:03.941371Z","signature_b64":"2c7przEgiOrsYzuByP6WSHJGQhyOjKaPodRIUqMIzrChiavyNnFAS4JUwaa+ud3N0BSzFnoxuv5WvA8kZTicCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3386d61ee482f5daf333da981995382ff4f465648208cb1cb764f401e684327","last_reissued_at":"2026-05-18T00:35:03.940680Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:03.940680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Object Detection Using Deep CNNs Trained on Synthetic Images","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hristo Bojinov, Param S. Rajpura, Ravi S. Hegde","submitted_at":"2017-06-21T08:16:29Z","abstract_excerpt":"The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications. We show that with transfer learning an effective object detector can be trained almost entirely on synthetically rendered datasets. We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic images achieves mean average precision (mAP) of 24 on a test set with 55 distinct products as objects of interest and 17 distractor objects. A further "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06782","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":""},"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":"1706.06782","created_at":"2026-05-18T00:35:03.940793+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06782v2","created_at":"2026-05-18T00:35:03.940793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06782","created_at":"2026-05-18T00:35:03.940793+00:00"},{"alias_kind":"pith_short_12","alias_value":"YM4G2YPOJAXV","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YM4G2YPOJAXV3LZT","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YM4G2YPO","created_at":"2026-05-18T12:31:56.362134+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/YM4G2YPOJAXV3LZTHWUYDGKTQL","json":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL.json","graph_json":"https://pith.science/api/pith-number/YM4G2YPOJAXV3LZTHWUYDGKTQL/graph.json","events_json":"https://pith.science/api/pith-number/YM4G2YPOJAXV3LZTHWUYDGKTQL/events.json","paper":"https://pith.science/paper/YM4G2YPO"},"agent_actions":{"view_html":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL","download_json":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL.json","view_paper":"https://pith.science/paper/YM4G2YPO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06782&json=true","fetch_graph":"https://pith.science/api/pith-number/YM4G2YPOJAXV3LZTHWUYDGKTQL/graph.json","fetch_events":"https://pith.science/api/pith-number/YM4G2YPOJAXV3LZTHWUYDGKTQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL/action/storage_attestation","attest_author":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL/action/author_attestation","sign_citation":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL/action/citation_signature","submit_replication":"https://pith.science/pith/YM4G2YPOJAXV3LZTHWUYDGKTQL/action/replication_record"}},"created_at":"2026-05-18T00:35:03.940793+00:00","updated_at":"2026-05-18T00:35:03.940793+00:00"}