Multi-choice checklist classification
Checklist classifications can be applied at the global level and can be nested within an object-type annotation. Use checklist classifications when more than option/answer is possible.
Import
Python SDK
Checklist
is a type of ClassificationAnnotation
Definition
Checklist(answer=List[ClassificationAnswer])
Parameter | Value |
---|---|
| List of |
Supported data types
Data type | Supported |
---|---|
Images, Video, Text, HTML, DICOM, Tiled and Document |
|
Checklist(answer=[ClassificationAnswer(name="first_checklist_answer"),ClassificationAnswer(name="second_checklist_answer")])
Importing nested classifications within classifications is currently not supported in the python SDK. Use NDJSON format instead.
NDJSON
NDJSON format is recommended if an annotation type is not yet supported in Python SDK or if you are unable to use the Python environment.
Definition
Parameter | Asset type | Required | Description |
---|---|---|---|
| Image | Yes | A user-generated UUID for each annotation. If you import an annotation to a Data Row and there is already an imported annotation with the same uuid on that Data Row, the latest import will override the previous one. The uuid must be 128 bits (32 characters). The following formats are supported:
|
| Image | Yes | The ID of the schema that contains all of the information needed for rendering your annotation. |
| Image | Yes | The ID of the Data Row where you want to attach the imported annotations. |
| Image | Yes | The ID of the Checklist answer schema. Checklist can have multiple correct answers so |
| Video | Yes | The first frame you wish to include in the checklist classification. |
| Video | Yes | The last frame you wish to include in the checklist classification. |
Supported data types
Data type | Supported |
---|---|
Images, Text, HTML, DICOM, Tiled and Document |
|
Video |
|
Format
// Global classification
{
"uuid": "fb72782d-f6ed-43ba-8677-77b03197392d",
"dataRow": {
"id": "ckd1299m8cqbs0cq43mju1bvp"
},
"schemaId": "ckd1295hc00640z0uapvm1xbd", // Checklist question
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // Checklist answer
},
{
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // Checklist answer
}
]
}
// Nested classification
{
"uuid": "532953e6-746f-4d74-945d-b4a9c2786479",
"schemaId": "ckshluz7h7d9b0yb60biaasya",
"dataRow": {
"id": "ckshkj1vj4rsq0yvubkoe76vi"
},
"bbox": {
"top": 57,
"left": 209,
"height": 216,
"width": 152
},
"classifications": [
{
"schemaId": "ckshluz847d9d0yb65oqhfg65", // Nested checklist question
"answers": [
{
"schemaId": "ckshluz8r7d9j0yb6d9l45o1b" // Nested checklist answer
},
{
"schemaId": "ckshluz8r7d9j0yb6d9l45opl" // Nested checklist answer
},
]
}
]
}
// Global classification only
{
"schemaId": "ckd1295hc00640z0uapvm1xbd", // question schema id
"uuid": "fb72782d-f6ed-43ba-8677-77b03197392d",
"dataRow": {
"id": "ckd1299m8cqbs0cq43mju1bvp"
},
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // answer schema id
}, {
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // answer schema id
}
],
"frames": [
{
"start": 7,
"end": 13,
},
{
"start": 18,
"end": 19,
}
]
}
// Global classification
{
"uuid": "fb72782d-f6ed-43ba-8677-77b03197392d",
"dataRow": {
"id": "ckd1299m8cqbs0cq43mju1bvp"
},
"schemaId": "ckd1295hc00640z0uapvm1xbd", // Checklist question
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // Checklist answer
},
{
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // Checklist answer
}
]
}
// Nested classification
{
"uuid": "9fd9a92e-2560-4e77-81d4-b2e955800092",
"schemaId": "ck8kukafkqx1a0880iczbrqym",
"dataRow": {
"id": "ck1s02fqxm8fi0757f0e6qtdc"
},
"location": {
"start": 67,
"end": 128
},
"classifications": [
{
"schemaId": "ckshluz847d9d0yb65oqhfg65", // Nested checklist question
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // Checklist answer
},
{
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // Checklist answer
}
]
}
]
}
// Global classification
{
"uuid": "fb72782d-f6ed-43ba-8677-77b03197392d",
"dataRow": {
"id": "ckd1299m8cqbs0cq43mju1bvp"
},
"schemaId": "ckd1295hc00640z0uapvm1xbd", // Checklist question
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // Checklist answer
},
{
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // Checklist answer
}
]
}
// Nested classification
{
"uuid": "532953e6-746f-4d74-945d-b4a9c2786479",
"schemaId": "ckshluz7h7d9b0yb60biaasya",
"dataRow": {
"id": "ckshkj1vj4rsq0yvubkoe76vi"
},
"bbox": {
"top": 57,
"left": 209,
"height": 216,
"width": 152
},
"classifications": [
{
"schemaId": "ckshluz847d9d0yb65oqhfg65", // Nested checklist question
"answers": [
{
"schemaId": "ckshluz8r7d9j0yb6d9l45o1b" // Nested checklist answer
},
{
"schemaId": "ckshluz8r7d9j0yb6d9l45opl" // Nested checklist answer
},
]
}
]
}
// Global classification
{
"uuid": "fb72782d-f6ed-43ba-8677-77b03197392d",
"dataRow": {
"id": "ckd1299m8cqbs0cq43mju1bvp"
},
"schemaId": "ckd1295hc00640z0uapvm1xbd", // Checklist question
"answers": [
{
"schemaId": "ckd1295jn00760z0u01hw4yz5" // Checklist answer
},
{
"schemaId": "ckd1295hh006g0z0ucbxgfgec" // Checklist answer
}
]
}
// Nested classification
{
"uuid": "532953e6-746f-4d74-945d-b4a9c2786479",
"schemaId": "ckshluz7h7d9b0yb60biaasya",
"dataRow": {
"id": "ckshkj1vj4rsq0yvubkoe76vi"
},
"bbox": {
"top": 57,
"left": 209,
"height": 216,
"width": 152
},
"classifications": [
{
"schemaId": "ckshluz847d9d0yb65oqhfg65", // Nested checklist question
"answers": [
{
"schemaId": "ckshluz8r7d9j0yb6d9l45o1b" // Nested checklist answer
},
{
"schemaId": "ckshluz8r7d9j0yb6d9l45opl" // Nested checklist answer
},
]
}
]
}
// Global classification
{
"uuid": "1278daa6-ce64-4363-be24-4fa5eadffb17",
"dataRow": {
"id": "ckd11jg6scq9c0cq43vmh6i07"
},
"schemaId": "ckd11j3yk000c0z0u4xn6dc4r", // Radio question
"answer": {
"schemaId": "ckd11j415000u0z0ubu7ee4w2" // Radio answer
}
}
// Nested classification
{
"uuid": "532953e6-746f-4d74-945d-b4a9c2786479",
"schemaId": "ckshluz7h7d9b0yb60biaasya",
"dataRow": {
"id": "ckshkj1vj4rsq0yvubkoe76vi"
},
"bbox": {
"top": 57,
"left": 209,
"height": 216,
"width": 152
},
"classifications": [
{
"schemaId": "ckshluz847d9d0yb65oqhfg65", // Nested radio question
"answer": {
"schemaId": "ckshluz8r7d9j0yb6d9l45o1b" // Nested radio answer
}
}
]
}
Export
When you export your Radio classifications from Labelbox, the export file will contain the following information for each Radio.
Python SDK
The export format of the polygon is similar to the import format.
Learn more about exporting annotations using the SDK here
JSON
You will receive a JSON file when you generate an export from the app.
Parameter | Asset type | Description |
---|---|---|
| Image | ID of the classification question in the ontology. |
| Image | ID of the schema that contains all of the structural information for the classification question. |
| Image | Text that appears as the classification question. |
| Image | Name of the classification question in the Labelbox database. |
| Image | ID of the classification answer in the ontology. |
| Image | ID of the schema that contains the structural information for the classification answer. |
| Image | Text that appears as the classification answer. |
| Image | Name of the classification answer in the Labelbox database. |
| Video | When |
Format
{
"featureId": "ckmuuwmp4000a3g68rmku827s",
"schemaId": "ckmuuvs5p5nj40y629l1570bi",
"title": "Is it daytime?",
"value": "is_it_daytime?",
"answer": {
"featureId": "ckmuuwmp400093g68qof3hnt8",
"schemaId": "ckmuuvs7u5njg0y629iiuc216",
"title": "Yes",
"value": "yes"
}
}
{
"frameNumber": 1,
"classifications": [{
"featureId": "cl2wkn4pe00023f6eskae8fgp",
"schemaId": "cl2wkmjoage4b07ae7anhhn1j",
"title": "Disease type",
"value": "disease_type",
"answer": {
"featureId": "cl2wknaex00053f6e0dr1cs3l",
"schemaId": "cl2wkmjoage4c07ae5vvv4pd2",
"title": "Type A",
"value": "type_a",
"keyframe": true
}
}],
"objects": [],
"relationships": []
},{
"frameNumber": 2,
"classifications": [{
"featureId": "cl2wkn4pe00023f6eskae8fgp",
"schemaId": "cl2wkmjoage4b07ae7anhhn1j",
"title": "Disease type",
"value": "disease_type",
"answer": {
"featureId": "cl2wknaex00053f6e0dr1cs3l",
"schemaId": "cl2wkmjoage4c07ae5vvv4pd2",
"title": "Type A",
"value": "type_a",
"keyframe": false
}
}],
"objects": [],
"relationships": []
}
{
"featureId": "cknp3d0hw00013g68wkjemi6o",
"schemaId": "cknp3ctst06nq0ycte8icc2l3",
"title": "Is it daytime?",
"value": "is_it_daytime?",
"answer": {
"featureId": "cknp3d0hw00003g685c0nj5c1",
"schemaId": "cknp3cttx06oe0yct3f0d6pmz",
"title": "Yes",
"value": "yes"
}
}
{
"featureId": "ckmuuwmp4000a3g68rmku827s",
"schemaId": "ckmuuvs5p5nj40y629l1570bi",
"title": "Is it daytime?",
"value": "is_it_daytime?",
"answer": {
"featureId": "ckmuuwmp400093g68qof3hnt8",
"schemaId": "ckmuuvs7u5njg0y629iiuc216",
"title": "Yes",
"value": "yes"
}
}
{
"featureId": "cknp3d0hw00013g68wkjemi6o",
"schemaId": "cknp3ctst06nq0ycte8icc2l3",
"title": "Is it daytime?",
"value": "is_it_daytime?",
"answer": {
"featureId": "cknp3d0hw00003g685c0nj5c1",
"schemaId": "cknp3cttx06oe0yct3f0d6pmz",
"title": "Yes",
"value": "yes"
}
}
{
"featureId": "ckmuuwmp4000a3g68rmku827s",
"schemaId": "ckmuuvs5p5nj40y629l1570bi",
"title": "Is it daytime?",
"value": "is_it_daytime?",
"answer": {
"featureId": "ckmuuwmp400093g68qof3hnt8",
"schemaId": "ckmuuvs7u5njg0y629iiuc216",
"title": "Yes",
"value": "yes"
}
}