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arnabdhar
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YOLOv8-nano-aadhar-card

Object Detection
PyTorch
ultralytics
yolov8
pickle
Eval Results
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metadata
license: apache-2.0
tags:
  - object-detection
  - ultralytics
  - yolov8
  - pytorch
  - pickle
pipeline_tag: object-detection
library_name: ultralytics
library_version: 8.0.198
model-index:
  - name: arnabdhar/YOLOv8-nano-aadhar-card
    results:
      - task:
          type: object-detection
        metrics:
          - type: precision
            value: 0.963
            name: mAP@50
          - type: precision
            value: 0.748
            name: mAP@50-95

YOLOv8 model to detect import texts on an Aadhar Card

Overview

Aadhaar Card text detection is the process of identifying and extracting text from Aadhaar Card images. This can be useful for a variety of applications, such as automatic data entry, fraud detection, and document verification.

One approach to Aadhaar Card text detection is to use YOLOv8, a state-of-the-art object detection model. YOLOv8 can be trained to detect a variety of object classes, including text. Once trained, YOLOv8 can be used to detect text in Aadhaar Card images and extract the text to a text file or other format.

Inference

Supported Labels 

# label_id: label_name
{0: "AADHAR_NUMBER", 1: "DATE_OF_BIRTH", 2: "GENDER", 3: "NAME", 4: "ADDRESS"}

Install Dependencies 

$ pip install ultralytics huggingface_hub supervision

Load the model 

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from supervision import Detections

# repo details
repo_config = dict(
    repo_id = "arnabdhar/YOLOv8-nano-aadhar-card",
    filename = "model.pt",
    local_dir = "./models"
)

# load model
model = YOLO(hf_hub_download(**repo_config))

# get id to label mapping
id2label = model.names
print(id2label)

# Perform Inference
image_url = "https://i.pinimg.com/originals/08/6d/82/086d820550f34066764f4047ddc263ca.jpg"

detections = Detections.from_ultralytics(model.predict(image_url)[0])

print(detections)

Fine Tuning

The following hyperparameters were used to finetune the model

model: yolov8n.pt
batch: 4
epochs: 100
optimizer: AdamW
warmup_epochs: 15
seed: 42
imgsz: 640

The following evaluation metrics were achieved by best.pt for bounding box predictions:

recall: 0.962
precision: 0.973
mAP50: 0.963
mAP50_95: 0.748

Dataset