| --- |
| license: apache-2.0 |
| tags: |
| - computer-vision |
| - image-segmentation |
| - welding |
| - weld-inspection |
| - yolo |
| --- |
| |
| # WeldVision Ensemble |
|
|
| Four-model YOLO segmentation ensemble for weld-surface defect inspection. |
|
|
| ## Models |
|
|
| - `weights/best.pt` |
| - `weights/best_v0.pt` |
| - `weights/crack_specialist.pt` |
| - `weights/spatters_specialist.pt` |
|
|
| Inference runs **locally** after downloading the weights from the Hugging Face |
| Hub. No Hugging Face Inference Endpoint is required. |
|
|
| ## Install |
|
|
| ```bash |
| pip install ultralytics huggingface_hub opencv-python-headless numpy |
| ``` |
|
|
| ## Use |
|
|
| ```python |
| from weldvision import WeldVision |
| |
| model = WeldVision.from_pretrained( |
| "bhavibhatt/weldvision-ensemble" |
| ) |
| |
| result = model.predict("weld.jpg") |
| |
| print(result) |
| ``` |
|
|
| The first call downloads and caches the four weights. Subsequent calls reuse the |
| local Hugging Face cache. |
|
|
| ## Important |
|
|
| This is a proof-of-concept weld surface inspection system. The score and |
| PASS/REVIEW/FAIL rules are prototype decision logic and are not welding-code |
| acceptance criteria or a substitute for qualified human inspection. |
|
|
