README.md

---
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()

```