Capture AI/ML Training Data
Steps how to capture field specific training data to improve accuracy of AI/ML predictions
Saral App internally uses AI/ML model to predict scanned layout ROIs(Region Of Interest). Current capabilities of this model is to detect handwritten digits and OMR answers. Internally model is trained with MNIST database. But as the handwritten digits can be sometimes field specific , this feature is handy to collect training data from production and improve accuracy of the model predictions.
Step 1: For a selected school , make sure storeTrainingData flag to be returned as true from backend API GET /schools.
{
"schools": [
{
"name": "Dummy school 1",
"schoolId": "up001",
"state": "up",
"district": "delhi",
"block": "haldwani",
"hmName": "acbc",
"noOfStudents": "100",
"storeTrainingData": true,
"createdAt": "2021-12-06T05:25:38.487Z",
"updatedAt": "2021-12-06T05:25:38.487Z"
}
]
}
Step 2: Saral frontend application will capture layout ROIs(Region of Interest) images and convert them into Base64 images and send them as part of PUT /saveMarks API. Saral App also compares the actual preidictions with user overrides if any , and populate training data only if necessary. This training data can be extracted from backend to train the AI/ML model to improve accuracy on iterative fashion.
Sample request from Saral App to backend APIPUT /saveMarks
{
"_id": {
"$oid": "619b38c8b18890002b7b186b"
},
"studentIdTrainingData": [
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"subject": "Hindi",
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}
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