variables
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1 row where datasetId = 6629 sorted by id descending
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id ▲ | name | unit | description | createdAt | updatedAt | code | coverage | timespan | datasetId | sourceId | shortUnit | display | columnOrder | originalMetadata | grapherConfigAdmin | shortName | catalogPath | dimensions | schemaVersion | processingLevel | processingLog | titlePublic | titleVariant | attributionShort | attribution | descriptionShort | descriptionFromProducer | descriptionKey | descriptionProcessing | licenses | license | grapherConfigETL | type | sort | dataChecksum | metadataChecksum |
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953970 | Top-1 accuracy | % | 2024-07-25 11:21:52 | 2024-07-25 12:17:07 | AI Performance on Imagenet 6629 | % | { "unit": "%", "zeroDay": "2019-01-01", "shortUnit": "%", "yearIsDay": true, "numDecimalPlaces": 1 } |
0 | top_1_accuracy | grapher/artificial_intelligence/2024-07-23/papers_with_code_imagenet/papers_with_code_imagenet#top_1_accuracy | 2 | major | [ "The top-1 accuracy measure is used to assess how frequently a model's absolute top prediction matches the correct answer from a given set of options.", "Here's an example to illustrate what this benchmark tests: Imagine an image classification model that is presented with an image of an animal. The model assigns probabilities to each potential label and generates its highest-confidence prediction. For instance, when analyzing an image, the model might predict 'Cat' as the most probable label. To evaluate the model's accuracy using the top-1 measure, researchers compare this prediction with the correct label. If the model's top prediction matches the correct label (e.g., if the actual animal in the image is indeed a cat), then the model's prediction is considered correct according to the top-1 accuracy metric. On the other hand, if the model's top prediction does not match the correct label (e.g., if the image shows a dog, but the model predicts a cat), then the model's prediction is considered incorrect based on the top-1 accuracy measure. To calculate the top-1 accuracy, researchers analyze the model's performance on a large dataset where the correct labels are known. They determine the percentage of examples in the dataset where the model's highest-confidence prediction matches the actual label.", "This measure provides a focused evaluation of the model's ability to make accurate predictions by considering only its absolute top guess." ] |
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f7fb48fbe151b4d27b926c8079c54471 | d6770393ef1449eb38f7880d50447a49 |
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CREATE TABLE "variables" ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" VARCHAR(750) NULL , "unit" VARCHAR(255) NOT NULL , "description" TEXT NULL , "createdAt" DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP , "updatedAt" DATETIME NULL , "code" VARCHAR(255) NULL , "coverage" VARCHAR(255) NOT NULL , "timespan" VARCHAR(255) NOT NULL , "datasetId" INTEGER NOT NULL , "sourceId" INTEGER NULL , "shortUnit" VARCHAR(255) NULL , "display" TEXT NOT NULL , "columnOrder" INTEGER NOT NULL DEFAULT '0' , "originalMetadata" TEXT NULL , "grapherConfigAdmin" TEXT NULL , "shortName" VARCHAR(255) NULL , "catalogPath" VARCHAR(767) NULL , "dimensions" TEXT NULL , "schemaVersion" INTEGER NOT NULL DEFAULT '1' , "processingLevel" VARCHAR(30) NULL , "processingLog" TEXT NULL , "titlePublic" VARCHAR(512) NULL , "titleVariant" VARCHAR(255) NULL , "attributionShort" VARCHAR(512) NULL , "attribution" TEXT NULL , "descriptionShort" TEXT NULL , "descriptionFromProducer" TEXT NULL , "descriptionKey" TEXT NULL , "descriptionProcessing" TEXT NULL , "licenses" TEXT NULL , "license" TEXT NULL , "grapherConfigETL" TEXT NULL , "type" TEXT NULL , "sort" TEXT NULL , "dataChecksum" VARCHAR(64) NULL , "metadataChecksum" VARCHAR(64) NULL, FOREIGN KEY("datasetId") REFERENCES "datasets" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT, FOREIGN KEY("sourceId") REFERENCES "sources" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT ); CREATE UNIQUE INDEX "idx_catalogPath" ON "variables" ("catalogPath"); CREATE UNIQUE INDEX "unique_short_name_per_dataset" ON "variables" ("shortName", "datasetId"); CREATE UNIQUE INDEX "variables_code_fk_dst_id_7bde8c2a_uniq" ON "variables" ("code", "datasetId"); CREATE INDEX "variables_datasetId_50a98bfd_fk_datasets_id" ON "variables" ("datasetId"); CREATE UNIQUE INDEX "variables_name_fk_dst_id_f7453c33_uniq" ON "variables" ("name", "datasetId"); CREATE INDEX "variables_sourceId_31fce80a_fk_sources_id" ON "variables" ("sourceId");