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print-defect-cnn

Zainulabedin1

A small convolutional neural network that classifies a photo of an FDM 3D print as a **good print** or a **stringing defect** (thin strands of filament left between printed sections — wasted material and post-processing time). 106k parameters, trained from scratch on CPU; input is a 64×64 grayscale image. Per-class F1 — good print: 80.56% · stringing: 97.60%. At the default threshold the model catches **every** stringing defect in the test set (100% recall on the defect class) at the cost of some false alarms on good prints — the right trade-off for defect screening, where a missed defect wastes a whole print. Preprocessing contract (must match training): convert to grayscale, resize to 64×64, `ToTensor()`, `Normalize(mean=0.5, std=0.5)`. The helper `predict_image` does this for you.

Image classification

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vit-casting-defect

Zainulabedin1

A **tiny Vision Transformer trained from scratch** (no pretraining, no ImageNet) that classifies steel surface-defect images into six defect classes. The point of the project is to show a ViT *can* be built for manufacturing and trained on CPU: 64x64 image -> 64 patches of 8x8 -> 2-layer transformer encoder -> class, ~113k parameters, trained on CPU in minutes. Per-class F1 (TinyViT): crazing 85.71% · patches 91.23% · pitted_surface 75.86% · inclusion 68.85% · scratches 68.97% · rolled-in_scale 66.67%. **Honest reading:** the small CNN wins by ~9 points — convolutional inductive bias beats a from-scratch transformer on a 1,440-image dataset. The ViT is still the point of the project: 113k parameters, no pretraining, CPU-trained in ~14 minutes, and 76% is far above the 16.7% chance level. On larger industrial datasets (or with a conv stem) the gap closes; see "What I'd try next" in the notebook.

Image classification

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