{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:2SVOBSKEKIXIXRLE24MFN5TVDF","short_pith_number":"pith:2SVOBSKE","canonical_record":{"source":{"id":"1907.06167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-14T05:01:31Z","cross_cats_sorted":[],"title_canon_sha256":"24ab84222c9887058b173f89cb863882d9cd3e12d110f7f57351fbaf6473c475","abstract_canon_sha256":"250bef75157854dea63d4a67d927e9d554aac349e5a6164c2343fd1fd8360e79"},"schema_version":"1.0"},"canonical_sha256":"d4aae0c944522e8bc564d71856f675195e69ffb0886ddda7251e64b9220461f1","source":{"kind":"arxiv","id":"1907.06167","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06167","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06167v1","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06167","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"pith_short_12","alias_value":"2SVOBSKEKIXI","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"2SVOBSKEKIXIXRLE","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"2SVOBSKE","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:2SVOBSKEKIXIXRLE24MFN5TVDF","target":"record","payload":{"canonical_record":{"source":{"id":"1907.06167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-14T05:01:31Z","cross_cats_sorted":[],"title_canon_sha256":"24ab84222c9887058b173f89cb863882d9cd3e12d110f7f57351fbaf6473c475","abstract_canon_sha256":"250bef75157854dea63d4a67d927e9d554aac349e5a6164c2343fd1fd8360e79"},"schema_version":"1.0"},"canonical_sha256":"d4aae0c944522e8bc564d71856f675195e69ffb0886ddda7251e64b9220461f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:38.333882Z","signature_b64":"iFBXcWLktMtjEhOx5YYFuoB8fnUYw3oS9VrT+Y8z7mX4a3hy/T4s5/UT3WAA9fxRCrH0mxst2mlK42QZ7Rg+Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4aae0c944522e8bc564d71856f675195e69ffb0886ddda7251e64b9220461f1","last_reissued_at":"2026-05-17T23:40:38.333375Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:38.333375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.06167","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vTwT4HL/1Cm79s9w5RFF/eSFT61nIvuPPtLhAMNQPkeO6sUE468PaGKvxcgmiUIG1Kj2ro8qJaxQctmCS/+8Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:54:57.680546Z"},"content_sha256":"674cf355b0dc621fa6242d8f662e3b17027ff1e1b7535c55f21c5e4337dd3060","schema_version":"1.0","event_id":"sha256:674cf355b0dc621fa6242d8f662e3b17027ff1e1b7535c55f21c5e4337dd3060"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:2SVOBSKEKIXIXRLE24MFN5TVDF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FoodX-251: A Dataset for Fine-grained Food Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ajay Divakaran, Karan Sikka, Parneet Kaur, Serge Belongie, Weijun Wang","submitted_at":"2019-07-14T05:01:31Z","abstract_excerpt":"Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating these models. In this paper we focus on the second aspect and introduce FoodX-251, a dataset of 251 fine-grained food categories with 158k images collected from the web. We use 118k images as a training set and provide human verified labels for 40k images that can be used for validat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06167","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pv3YkJk+uouIwUtg+BkxWSuZRb5x93AVOOgNjdnXAnXsZbDkiQO8TM6KUMLYMRSFwod51gLSu/+h5BMdh7aTBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:54:57.681269Z"},"content_sha256":"5e606c672b335949aa767e5521cb05bca957e109557d54534e04355503ca043c","schema_version":"1.0","event_id":"sha256:5e606c672b335949aa767e5521cb05bca957e109557d54534e04355503ca043c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/bundle.json","state_url":"https://pith.science/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T11:54:57Z","links":{"resolver":"https://pith.science/pith/2SVOBSKEKIXIXRLE24MFN5TVDF","bundle":"https://pith.science/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/bundle.json","state":"https://pith.science/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2SVOBSKEKIXIXRLE24MFN5TVDF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2SVOBSKEKIXIXRLE24MFN5TVDF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"250bef75157854dea63d4a67d927e9d554aac349e5a6164c2343fd1fd8360e79","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-14T05:01:31Z","title_canon_sha256":"24ab84222c9887058b173f89cb863882d9cd3e12d110f7f57351fbaf6473c475"},"schema_version":"1.0","source":{"id":"1907.06167","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06167","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06167v1","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06167","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"pith_short_12","alias_value":"2SVOBSKEKIXI","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"2SVOBSKEKIXIXRLE","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"2SVOBSKE","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:5e606c672b335949aa767e5521cb05bca957e109557d54534e04355503ca043c","target":"graph","created_at":"2026-05-17T23:40:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating these models. In this paper we focus on the second aspect and introduce FoodX-251, a dataset of 251 fine-grained food categories with 158k images collected from the web. We use 118k images as a training set and provide human verified labels for 40k images that can be used for validat","authors_text":"Ajay Divakaran, Karan Sikka, Parneet Kaur, Serge Belongie, Weijun Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-14T05:01:31Z","title":"FoodX-251: A Dataset for Fine-grained Food Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06167","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:674cf355b0dc621fa6242d8f662e3b17027ff1e1b7535c55f21c5e4337dd3060","target":"record","created_at":"2026-05-17T23:40:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"250bef75157854dea63d4a67d927e9d554aac349e5a6164c2343fd1fd8360e79","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-14T05:01:31Z","title_canon_sha256":"24ab84222c9887058b173f89cb863882d9cd3e12d110f7f57351fbaf6473c475"},"schema_version":"1.0","source":{"id":"1907.06167","kind":"arxiv","version":1}},"canonical_sha256":"d4aae0c944522e8bc564d71856f675195e69ffb0886ddda7251e64b9220461f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4aae0c944522e8bc564d71856f675195e69ffb0886ddda7251e64b9220461f1","first_computed_at":"2026-05-17T23:40:38.333375Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:38.333375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iFBXcWLktMtjEhOx5YYFuoB8fnUYw3oS9VrT+Y8z7mX4a3hy/T4s5/UT3WAA9fxRCrH0mxst2mlK42QZ7Rg+Cg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:38.333882Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.06167","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:674cf355b0dc621fa6242d8f662e3b17027ff1e1b7535c55f21c5e4337dd3060","sha256:5e606c672b335949aa767e5521cb05bca957e109557d54534e04355503ca043c"],"state_sha256":"7c2282797fdba3156dd02ed4588f106fd4c30c387b8a60f2c4d29e6c5bf02d1c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BsF9qxL63Rl3cpagyQtu6lhGFvaALnW1AIzIhQCTKo79d/txc1SgvPeCa83MwIdqCU7CPaYnRUcV17lhO/GmBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T11:54:57.684950Z","bundle_sha256":"7fee1ceade89e3ce3093f4bcb2bf9d948b98bc705c2d5c7108f9535c7cb1158b"}}