Image Segmentation
ultralytics
TensorBoard
PyTorch
v8
ultralyticsplus
yolov8
yolo
vision
awesome-yolov8-models
Eval Results (legacy)
Instructions to use keremberke/yolov8n-pcb-defect-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use keremberke/yolov8n-pcb-defect-segmentation with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("keremberke/yolov8n-pcb-defect-segmentation", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from keremberke/yolov8n-pcb-defect-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/keremberke/yolov8n-pcb-defect-segmentation/resolve/main/config.json
- Command line
-
hf download hf://keremberke/yolov8n-pcb-defect-segmentation/config.json
-
curl -L -o config.json https://huggingface.co/keremberke/yolov8n-pcb-defect-segmentation/resolve/main/config.json
195 Bytes
| {"input_size": 640, "task": "instance-segmentation", "ultralyticsplus_version": "0.0.23", "ultralytics_version": "8.0.21", "model_type": "v8", "score_map50": 0.51186, "score_map50_mask": 0.51667} |