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A PCB analysis tool that uses a referential approach for localization and identification of PCB defect, by leveraging an EfficientNet-b4 classifier, to detect defects in a PCB board. This project is part of my undergrad thesis project. Further details can be found in the Final_Year_Project_Thesis-Google Docs.pdf.

Python 4.78% CSS 0.28% HTML 5.37% Jupyter Notebook 89.57%
deep-learning electronics final-year-project machine-learning pcb pcb-design pcb-layout python

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pcb_defect_analysis's Issues

RuntimeError: boxes should be a 2d tensor, got 1D.

I tried to upload two pictures, but I got an error. RuntimeError: boxes should be a 2d tensor, got 1D.
Traceback (most recent call last):
File "D:/detection/pcb_defect_analysis-master/model_inference/inference.py", line 135, in
result = model_inference(model, path_1, path_2)
File "D:/detection/pcb_defect_analysis-master/model_inference/inference.py", line 99, in model_inference
r1 = extract_contours_from_image(img1)
File "D:/detection/pcb_defect_analysis-master/model_inference/inference.py", line 55, in extract_contours_from_image
inds = list(nms(boxes=boxes, scores=torch.Tensor([1] * len(regions)), iou_threshold=0.1).numpy())
File "C:\Users\ASUS\Anaconda3\lib\site-packages\torchvision\ops\boxes.py", line 36, in nms
return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
RuntimeError: boxes should be a 2d tensor, got 1D

so, Is the way I execute the code wrong? could you tell me how to run it

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