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Updated 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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@@ -15,7 +19,14 @@ Aadhaar Card text detection is the process of identifying and extracting text fr
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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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- ## Getting Started with Inference
 
 
 
 
 
 
 
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  ### Install Dependencies
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@@ -28,7 +39,56 @@ $ pip install ultralytics huggingface_hub supervision
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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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- # l.oad model
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- ```
 
 
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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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+
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+ ### Supported Labels
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+
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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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+
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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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+
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+ # load model
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+ model = YOLO(hf_hub_download(**repo_config))
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+
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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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+
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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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+
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+ detections = Detections.from_ultralytics(model.predict(image_url)[0])
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+
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+ print(detections)
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+
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+ ```
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+
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+ ## Fine Tuning
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+
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+ The following hyperparameters were used to finetune the model
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+
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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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+
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+ The following evaluation metrics were achieved by `best.pt` for bounding box predictions:
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+
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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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+
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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