File size: 2,440 Bytes
d4cd784 5e36791 d4cd784 35d1523 d4cd784 35d1523 d4cd784 35d1523 d5f938d 35d1523 d4cd784 5e36791 d4cd784 5e36791 d4cd784 5e36791 d4cd784 5e36791 d5f938d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
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
```python
# label_id: label_name
{0: "AADHAR_NUMBER", 1: "DATE_OF_BIRTH", 2: "GENDER", 3: "NAME", 4: "ADDRESS"}
```
### Install Dependencies
```bash
$ pip install ultralytics huggingface_hub supervision
```
### Load the model
```python
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
```yaml
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:
```yaml
recall: 0.962
precision: 0.973
mAP50: 0.963
mAP50_95: 0.748
```
## Dataset
+ __Source__: Roboflow Universe
+ __Dataset URL__: https://universe.roboflow.com/jizo/aadhar-card-entity-detection
|