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---
tags:
- ultralyticsplus
- yolov5
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
- indonesia
- aksara
- aksarajawa
model-index:
- name: hermanshid/yolo-aksara-jawa
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.995 # min: 0.0 - max: 1.0
name: [email protected](box)
inference: false
---
# YOLOv5 for Aksara Jawa
<div align="center">
<img width="640" alt="hermanshid/aksarajawa" src="https://huggingface.co/hermanshid/yolo-aksara-jawa/resolve/main/thumbnail.jpg">
</div>
## Dataset
Dataset available in [kaggle](https://www.kaggle.com/datasets/hermansugiharto/aksara-jawa-yolo-v5-dataset)
## Supported Labels
```python
[
"ba", "ca", "da", "dha", "ga", "ha", "ja", "ka", "la", "ma",
"na", "nga", "nya", "pa", "ra", "sa", "ta", "tha", "wa", "ya"
]
```
## How to use
- Install library
`pip install yolov5==7.0.5 torch`
## Load model and perform prediction
```python
import yolov5
from PIL import Image
model = yolov5.load(models_id)
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://huggingface.co/spaces/hermanshid/aksara-jawa-space/raw/main/test_images/example1.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
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