ViT Overfits Small Datasets: When CNNs Win by 18% mAP

#visiontransformer #cnn #overfitting #smalldataset

Vision Transformers Need 10x More Data Than You Think

I trained a ViT-Base/16 on 2,000 images and watched it collapse. Training loss dropped to 0.03 while validation accuracy flatlined at 62%. The same ResNet-50 baseline hit 80% with zero signs of overfitting.

Vision Transformers dominate ImageNet leaderboards, but that 1.2M-image scale hides a critical flaw: ViTs overfit brutally on small datasets. If you're working with under 10K images — medical scans, industrial defect detection, custom object classes — CNNs still win on accuracy, training stability, and inference cost. Here's the data that changed how I pick architectures.

Close-up of an electrical transformer on a utility pole against a sunset sky. — Photo by Mario Amé on Pexels

The Inductive Bias Gap: Why ViTs Learn Slower

CNNs bake in spatial priors through convolution: translation equivariance, local receptivity, hierarchical features. A 3×3 kernel "knows" that edges matter more than pixel relationships 50 pixels apart. ViTs throw this away. Self-attention computes pairwise relationships between all $N$ patches with $O(N^2)$ complexity, learning spatial structure from scratch.

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