Inductive Bias vs Scaling Laws
CNNs have an inductive bias for translation invariance—they inherently understand that a cat in the top left is the same as a cat in the bottom right. ViTs lack this and must learn it from massive data. Thus, ViTs win when data is essentially infinite, but CNNs win on small, custom datasets.
Deployment Hardware Constraints
If you are deploying to a Raspberry Pi, mobile phone, or embedded device, architectures like MobileNet, EfficientNet, or YOLOv8 are heavily optimized for edge inference (NPU/DSP acceleration). ViTs often struggle with latency on edge hardware due to attention mechanism overhead.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape