Object Detection
ultralytics
TensorBoard
PyTorch
v8
ultralyticsplus
yolov8
yolo
vision
Eval Results (legacy)
Instructions to use linhcuem/chamdiemgianhang_yolov8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use linhcuem/chamdiemgianhang_yolov8 with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("linhcuem/chamdiemgianhang_yolov8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Supported Labels
['bom_gen', 'bom_jn', 'bom_knp', 'bom_sachet', 'bom_vtgk', 'bom_ytv', 'hop_jn', 'hop_vtg', 'hop_ytv', 'lo_kid', 'lo_ytv', 'loc_dln', 'loc_jn', 'loc_kids', 'loc_ytv', 'pocky', 'tui_gen', 'tui_jn', 'tui_sachet', 'tui_vtgk']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('linhcuem/chamdiemgianhang_yolov8')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.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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Evaluation results
- mAP@0.5(box)self-reported0.863