{"id":23557066,"url":"https://github.com/verisilicon/acuity-dataset","last_synced_at":"2025-07-10T07:37:01.520Z","repository":{"id":85936187,"uuid":"114296715","full_name":"VeriSilicon/acuity-dataset","owner":"VeriSilicon","description":"Acuity Dataset scripts and examples ","archived":false,"fork":false,"pushed_at":"2019-01-29T06:17:14.000Z","size":72392,"stargazers_count":6,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-30T07:47:06.471Z","etag":null,"topics":["acuity","deep-learning","neural-network"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VeriSilicon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2017-12-14T21:13:21.000Z","updated_at":"2024-11-20T02:37:38.000Z","dependencies_parsed_at":null,"dependency_job_id":"1963e398-0302-4cca-92c5-94a31822e34a","html_url":"https://github.com/VeriSilicon/acuity-dataset","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/VeriSilicon/acuity-dataset","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VeriSilicon%2Facuity-dataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VeriSilicon%2Facuity-dataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VeriSilicon%2Facuity-dataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VeriSilicon%2Facuity-dataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VeriSilicon","download_url":"https://codeload.github.com/VeriSilicon/acuity-dataset/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VeriSilicon%2Facuity-dataset/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264545256,"owners_count":23625408,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["acuity","deep-learning","neural-network"],"created_at":"2024-12-26T14:19:13.122Z","updated_at":"2025-07-10T07:37:01.512Z","avatar_url":"https://github.com/VeriSilicon.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ACUITY Dataset Examples\n\nThis package contains information about the database format used by ACUITY\nfor storing Training and Testing images. Sample scripts for ImageNet and \nPASCAL VOC are also provided. \n\n## ACUITY supported dataset\n### SQLite Database format\nImageNet Example (SQlite)\n   \n\u003e See ReadMe.md in ImageNet folder for details\n   \nPASCAL Example (SQlite)\n   \n\u003e See ReadMe.md in PASCAL folder for details\n\nMNIST and INRIA Example (SQlite)\n\n\u003e Pre-built Dataset for Mnist and INRIA\n\n### Text format\nImageNet Exmaple (Text)\n\n\u003e ImageNet/dataset.txt contains example of the text dataset format\n\nILSVRC2012_VAL_SET (Text)\n\n\u003e ILSVRC2012_VAL_SET/dataset.txt contains a pre-built small validation set of images\n  from ILSVRC2012 ImageNet\n\n## SQLite Database Format\n\n   Default.dsx is an empty ACUITY SQlite dataset file\n\n    # Use sqlitebrowser to view Dataset schema and content\n    sqlitebrowser Default.dsx\n\n    # Tables\n    There are 5 tables in the Dataset\n\n    \"files\" Table\n    This table contains the path to the resource files (typically Images) in the\n    Database.\n        -id: Unique key identifying the file\n        -name: File name without extension\n        -ext: File extension\n\n    \"groups\" Table\n    This table contains the group entry - \"Training\" and \"Testing\"\n        - id-name: 1-Training, 2-Testing\n\n    \"labels\" Table\n    This table contains the labels or classes for associated with the dataset. For\n    example, ILSVRC dataset contains 1000 classes with each class identifying a\n    particular type of objects \n        - id: Unique key identifying the label\n        - name: String name for the label\n        - description: String description\n\n    \"boxed_annotations\" Table\n    This is the master table which contains information about all the images in the\n    dataset using the metadata in the other tables. \n    \n        - id: Unique key identifying this annotation\n        - label_id: label id associated with this annotation\n        - group_id: Identify whether this annotation is used for Training or Testing\n        - file_id: The image file for this annotation\n        - x/y/w/h: ROI window from the image where this annotation is located\n\n    \"validation_results\" Table\n    This table stores the validation result from running Validation of the network\n        - id: Unique key identifying the result\n        - session_timestamp: Time Stamp for the inference\n        - file_id: Image file used for this inference\n        - boxed_annotation: id key into the box_annotation table\n        - expected_label/calculated_label\n        - score: Top1 score of the inference\n        - result_vector: Values from the output vector of the network\n \n## Text Dataset format\n    \n    Text Dataset format is a comma separated text file with each line containing\n    the file location and label.\n\n```shell\n ./data/ILSVRC2012_val_00000347.JPEG, 472, 0 0 28 28, validate # Use for validate only, crop pad \"0,0,28,28\", lable is 472\n ./data/ILSVRC2012_val_00000355.JPEG, 260, 1 1 29 29, train    # Use for traine only, crop pad \"1,1,29,29\", lable is 260\n ./data/ILSVRC2012_val_00000361.JPEG, 300, , train             # Use for train only, no crop pad, label is 300\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fverisilicon%2Facuity-dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fverisilicon%2Facuity-dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fverisilicon%2Facuity-dataset/lists"}