Instructions to use HanzalaTech/industrial-vision-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use HanzalaTech/industrial-vision-model 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("HanzalaTech/industrial-vision-model", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Industrial Vision Model (Ron 88)
YOLOv8n detector for Ron 88 bottle inspection.
Classes
| id | name |
|---|---|
| 0 | bottle_ron88 |
| 1 | bottle_other_brand |
| 2 | defect_low_fill |
| 3 | defect_no_cap |
| 4 | defect_loose_cap |
| 5 | defect_debris |
| 6 | defect_label_damage |
Usage
from ultralytics import YOLO
model = YOLO("HanzalaTech/industrial-vision-model")
results = model.predict("image.jpg")
Or download best.pt from this repo and load it locally:
from ultralytics import YOLO
model = YOLO("best.pt")
- Downloads last month
- 26