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--- |
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license: apache-2.0 |
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tags: |
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- object-detection |
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- ultralytics |
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- yolov8 |
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- pytorch |
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- pickle |
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pipeline_tag: object-detection |
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library_name: ultralytics |
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library_version: 8.0.198 |
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model-index: |
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- name: arnabdhar/YOLOv8-nano-aadhar-card |
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results: |
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- task: |
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type: object-detection |
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metrics: |
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- type: precision |
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value: 0.963 |
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name: mAP@50 |
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- type: precision |
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value: 0.748 |
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name: mAP@50-95 |
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--- |
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# YOLOv8 model to detect import texts on an Aadhar Card |
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## Overview |
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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. |
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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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```bash |
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$ pip install ultralytics huggingface_hub supervision |
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``` |
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### Load the model |
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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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