Updated README.md
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README.md
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---
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tags:
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- ultralytics
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- yolov8
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- object-detection
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- pytorch
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library_name: ultralytics
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library_version: 8.0.198
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---
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# YOLOv8 model to detect import texts on an Aadhar Card
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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.
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##
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### Install Dependencies
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```python
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from ultralytics import YOLO
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from huggingface_hub import hf_hub_download
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---
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license: apache-2.0
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tags:
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- ultralytics
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- yolov8
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- pytorch
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pipelline_tag: object-detection
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library_name: ultralytics
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library_version: 8.0.198
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metrics:
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- recall
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- precision
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---
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# YOLOv8 model to detect import texts on an Aadhar Card
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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.
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## Inference
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### Supported Labels
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```python
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# label_id: label_name
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{0: "AADHAR_NUMBER", 1: "DATE_OF_BIRTH", 2: "GENDER", 3: "NAME", 4: "ADDRESS"}
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```
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### Install Dependencies
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```python
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from ultralytics import YOLO
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from huggingface_hub import hf_hub_download
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from supervision import Detections
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# repo details
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repo_config = dict(
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repo_id = "arnabdhar/YOLOv8-nano-aadhar-card",
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filename = "model.pt",
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local_dir = "./models"
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)
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# load model
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model = YOLO(hf_hub_download(**repo_config))
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# get id to label mapping
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id2label = model.names
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print(id2label)
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# Perform Inference
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image_url = "https://i.pinimg.com/originals/08/6d/82/086d820550f34066764f4047ddc263ca.jpg"
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detections = Detections.from_ultralytics(model.predict(image_url)[0])
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print(detections)
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```
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## Fine Tuning
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The following hyperparameters were used to finetune the model
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```yaml
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model: yolov8n.pt
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batch: 4
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epochs: 100
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optimizer: AdamW
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warmup_epochs: 15
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seed: 42
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imgsz: 640
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```
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The following evaluation metrics were achieved by `best.pt` for bounding box predictions:
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```yaml
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recall: 0.962
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precision: 0.973
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mAP50: 0.963
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mAP50_95: 0.748
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```
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## Dataset
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+ __Source__: Roboflow Universe
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+ __Dataset URL__: https://universe.roboflow.com/jizo/aadhar-card-entity-detection
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