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Image classification

Alex Cota Updated by Alex Cota

There are two ways to configure the classification tool: classification only and nested classification on an object.

Follow the steps below once you create a project and upload your image data.

Set up classification only
  1. Bypass the "Add Object" step.
  2. Add a classification question(s).
  3. Select answer choice type and add answer(s).
  4. Click "Confirm".
Set up nested classification on an object
  1. Click "Add Object".
  2. Name your object and choose a labeling tool from the dropdown menu.
  3. Click on the right arrow on the object to open the Settings for that object.
  4. Add a classification question(s).
  5. Select answer choice type and add answer(s).
  6. Click "Done".

The classification tool only produces a semantic classification and does not include any geometric annotation or pixel classification information. There are four ways for labelers to provide a value for classification: radio, checklist, dropdown, and free-form text.

Radio

Use the radio format if you would like a labeler to choose a single answer from a selection of answer choices. The nested radio format allows the labeler to select multiple answers from a nested taxonomy.

This is what your JSON file will look like when you export classifications configured with radio answer choices.

//Radio
"classifications": [
{
"featureId": "ck9bloham1d1b0yf5870yxzxp",
"schemaId": "ck9blmq4ifi4b09760b4ur5ih",
"title": "Radio question",
"value": "radio_question",
"answer": {
"featureId": "ck9blohbp1d1c0yf523u34azb",
"schemaId": "ck9blmq1lnlxo0889u50lm3dx",
"title": "Yes",
"value": "yes"
}
}
]

Checklist

Use the checklist format if you would like a labeler to choose multiple answers from a selection of answer choices.

This is what your JSON file will look like when you export classifications configured with checklist.

//Checklist
"classifications": [
{
"featureId": "ck9bloipe005h10evp9txzhi6",
"schemaId": "ck9blmq4jfi4c0976jzdlk2fw",
"title": "Checklist question",
"value": "checklist_question",
"answers": [
{
"featureId": "ck9bloiqc005i10evlzlv90v5",
"schemaId": "ck9blmq1lnlxq0889oy9h5596",
"title": "Red",
"value": "red"
},
{
"featureId": "ck9blojbm1doo0zdga69rzqdj",
"schemaId": "ck9blmq1lnlxr08893iamqxcz",
"title": "Blue",
"value": "blue"
}
]
}
]

The nested dropdown allows labelers to select answers from a nested taxonomy. This is what your JSON file will look like when you export classifications configured with dropdown.

"classifications": [
{
"featureId": "ck9nedyfw05v91276aqbzfsnj",
"schemaId": "ck9nedt5a1kgw0y6tnj17m2i2",
"title": "Dropdown question",
"value": "dropdown_question",
"answer": [
{
"featureId": "ck9nedyhm05va127627ypedfy",
"schemaId": "ck9nedt701kh20y6tqoxgndvv",
"title": "Answer",
"value": "answer"
},
{
"featureId": "ck9nedyi305vb12764xxeuypd",
"schemaId": "ck9nedt8a1khb0y6t37ylx1rb",
"title": "Nested answer",
"value": "nested_answer"
}
]
}
]

Free-form text

Use the text classification format if you would like a labeler to enter a free-form text answer in response to a classification question.

There are various cases where you might want to use free-form text input such as for optical character recognition (OCR) tasks. Often OCR tasks have use the bounding box tool along with the nested free-form text classification tool.

This is what your JSON file will look like when you export classifications configured with free-form text answer choices.

// Text classification
"classifications": [
{
"featureId": "ck9bloche1fy910bom8fgji90",
"schemaId": "ck9blmq1lnlxs0889u7xa8byw",
"title": "Free text question",
"value": "free_text_question",
"answer": "sample text"
}
]

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