{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HVUXVEJMAFIVAFVLWSCFEVOMZT","short_pith_number":"pith:HVUXVEJM","schema_version":"1.0","canonical_sha256":"3d697a912c01515016abb4845255ccccdead4d828930e6341488aeb049651aa4","source":{"kind":"arxiv","id":"1707.08816","version":1},"attestation_state":"computed","paper":{"title":"Food Ingredients Recognition through Multi-label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aina Ferr\\`a, Marc Bola\\~nos, Petia Radeva","submitted_at":"2017-07-27T11:16:42Z","abstract_excerpt":"Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a method for adapting a highly performing state of the art CNN in order to act as a multi-label predictor for learning recipes in terms of their list of ingredients. We prove that our model is able to, given a picture, predict its list of ingredients, even if the recipe corresponding to the picture has never been seen by the model. We make public two new datasets suitable for this "},"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":"1707.08816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-27T11:16:42Z","cross_cats_sorted":[],"title_canon_sha256":"ae28dc32d5c2582454b3210246327e9e7de9ae549f9c83dcac28d936535f79d9","abstract_canon_sha256":"50a97af60ffa127018b201ba2fb7843a7560e7a88a6f66d45a6b41d7bdbc081e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:19.399549Z","signature_b64":"yVxDJoU1r+dWvjt35e96RbB0AeNiBdlx9Ko4tnBNxzHtfN75bGgAOX1PUZiHaMB4r+5s6RAydSI520ekl0++Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d697a912c01515016abb4845255ccccdead4d828930e6341488aeb049651aa4","last_reissued_at":"2026-05-18T00:39:19.398821Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:19.398821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Food Ingredients Recognition through Multi-label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aina Ferr\\`a, Marc Bola\\~nos, Petia Radeva","submitted_at":"2017-07-27T11:16:42Z","abstract_excerpt":"Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a method for adapting a highly performing state of the art CNN in order to act as a multi-label predictor for learning recipes in terms of their list of ingredients. We prove that our model is able to, given a picture, predict its list of ingredients, even if the recipe corresponding to the picture has never been seen by the model. We make public two new datasets suitable for this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.08816","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":""},"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":"1707.08816","created_at":"2026-05-18T00:39:19.398941+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.08816v1","created_at":"2026-05-18T00:39:19.398941+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.08816","created_at":"2026-05-18T00:39:19.398941+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVUXVEJMAFIV","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVUXVEJMAFIVAFVL","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVUXVEJM","created_at":"2026-05-18T12:31:21.493067+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/HVUXVEJMAFIVAFVLWSCFEVOMZT","json":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT.json","graph_json":"https://pith.science/api/pith-number/HVUXVEJMAFIVAFVLWSCFEVOMZT/graph.json","events_json":"https://pith.science/api/pith-number/HVUXVEJMAFIVAFVLWSCFEVOMZT/events.json","paper":"https://pith.science/paper/HVUXVEJM"},"agent_actions":{"view_html":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT","download_json":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT.json","view_paper":"https://pith.science/paper/HVUXVEJM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.08816&json=true","fetch_graph":"https://pith.science/api/pith-number/HVUXVEJMAFIVAFVLWSCFEVOMZT/graph.json","fetch_events":"https://pith.science/api/pith-number/HVUXVEJMAFIVAFVLWSCFEVOMZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT/action/storage_attestation","attest_author":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT/action/author_attestation","sign_citation":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT/action/citation_signature","submit_replication":"https://pith.science/pith/HVUXVEJMAFIVAFVLWSCFEVOMZT/action/replication_record"}},"created_at":"2026-05-18T00:39:19.398941+00:00","updated_at":"2026-05-18T00:39:19.398941+00:00"}