--- 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 mAP@0.5 is not available on hf.co/metrics value: 0.995 # min: 0.0 - max: 1.0 name: mAP@0.5(box) inference: false --- # YOLOv5 for Aksara Jawa
hermanshid/aksarajawa
## 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() ```