Instructions to use StevenHuang233/PCB_ObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use StevenHuang233/PCB_ObjectDetection 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("StevenHuang233/PCB_ObjectDetection", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 801 Bytes
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tags:
- object-detection
- pcb
- yolov11
- rt-detr
- ultralytics
---
# PCB Object Detection Checkpoints
PCB 缺陷目标检测训练结果与模型权重。
## 包含的训练结果
- RT-DETR:`rtdetr-AKConv`
- YOLOv11:`yolo11n`、`yolo11s`、`yolo11m`、`yolo11L`、`yolo11x`
每个训练目录保留了训练配置、指标、可视化结果及 `weights` 下的模型权重。
## 目录结构
```text
rt-detr/runs/train/rtdetr-AKConv/
yolov11/runs/train/yolo11n/
yolov11/runs/train/yolo11s/
yolov11/runs/train/yolo11m/
yolov11/runs/train/yolo11L/
yolov11/runs/train/yolo11x/
```
权重文件包括 `best.pt`、`last.pt`,YOLOv11 目录还包含相应的 FP32 权重。
## 下载
```powershell
hf download jjhhjj/PCB_ObjectDetection --local-dir .\PCB_ObjectDetection
```
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