{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CONL6PQENQFI3WOVEYMLUOB6R5","short_pith_number":"pith:CONL6PQE","schema_version":"1.0","canonical_sha256":"139abf3e046c0a8dd9d52618ba383e8f7cfb21acf9491d0f328ed11dec93331a","source":{"kind":"arxiv","id":"1705.07962","version":2},"attestation_state":"computed","paper":{"title":"pix2code: Generating Code from a Graphical User Interface Screenshot","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Tony Beltramelli","submitted_at":"2017-05-22T19:32:20Z","abstract_excerpt":"Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies)."},"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":"1705.07962","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2017-05-22T19:32:20Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","cs.NE"],"title_canon_sha256":"2e9d17d341fc267eafe5a39d4aa2b8af5f205909a88467f8f8ffb1f1bc0c1a64","abstract_canon_sha256":"5c8efb57ca5f1b240da1535650ac9017a83c0a4e3143eb55551ab8b15801d83f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:57.183271Z","signature_b64":"6/WwZm9WkhX+svOiY/sBw+SIrPwvy+/nlk9NmccIg12pMdUhU85NaORiKS1bRrvlRyUCCMMZBaq7HNTKXjmnCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"139abf3e046c0a8dd9d52618ba383e8f7cfb21acf9491d0f328ed11dec93331a","last_reissued_at":"2026-05-18T00:34:57.182647Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:57.182647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"pix2code: Generating Code from a Graphical User Interface Screenshot","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Tony Beltramelli","submitted_at":"2017-05-22T19:32:20Z","abstract_excerpt":"Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07962","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":"1705.07962","created_at":"2026-05-18T00:34:57.182741+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.07962v2","created_at":"2026-05-18T00:34:57.182741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07962","created_at":"2026-05-18T00:34:57.182741+00:00"},{"alias_kind":"pith_short_12","alias_value":"CONL6PQENQFI","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CONL6PQENQFI3WOV","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CONL6PQE","created_at":"2026-05-18T12:31:10.602751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11680","citing_title":"ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19742","citing_title":"PlayCoder: Making LLM-Generated GUI Code Playable","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5","json":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5.json","graph_json":"https://pith.science/api/pith-number/CONL6PQENQFI3WOVEYMLUOB6R5/graph.json","events_json":"https://pith.science/api/pith-number/CONL6PQENQFI3WOVEYMLUOB6R5/events.json","paper":"https://pith.science/paper/CONL6PQE"},"agent_actions":{"view_html":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5","download_json":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5.json","view_paper":"https://pith.science/paper/CONL6PQE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.07962&json=true","fetch_graph":"https://pith.science/api/pith-number/CONL6PQENQFI3WOVEYMLUOB6R5/graph.json","fetch_events":"https://pith.science/api/pith-number/CONL6PQENQFI3WOVEYMLUOB6R5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5/action/storage_attestation","attest_author":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5/action/author_attestation","sign_citation":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5/action/citation_signature","submit_replication":"https://pith.science/pith/CONL6PQENQFI3WOVEYMLUOB6R5/action/replication_record"}},"created_at":"2026-05-18T00:34:57.182741+00:00","updated_at":"2026-05-18T00:34:57.182741+00:00"}