Are Convolutional Neural Networks dead? Long live CNNs!
Transformers look like the default in 2025 , sure. But every time I try to ship something on a tiny CPU budget or a finicky edge device, I remember why convolutions still slap. This post is my quick tour — not a paper, just the way I explain it when someone asks, “So… are CNNs over?”
The CNN Years
I still remember when AlexNet blew up ImageNet in 2012. It felt like someone opened a window in a stale room — suddenly GPUs mattered for vision, and everyone started stacking convs like LEGO. Then came VGG (make it deeper), Inception (make it smarter), ResNet (make it actually train), and YOLO , which, honestly, was the first time object detection felt like something you could train on a weeknight without crying.
What made YOLO special wasn’t just speed; it was the vibe. Open weights, simple configs, “here’s how you fine-tune on your stuff.” You didn’t need a cluster — just a plan and a bit of stubbornness.
But there was always a silent villain: the dataset .
If your dogs are outnumbered by cats 1 to 9 , the model learns “four legs → probably a cat,” and you still get 90% accuracy . Gold star on the benchmark, paper accepted — and then it fails the first time someone brings in a corgi. When classes get fuzzy ( predator vs prey ), “worldwide dataset” usually means “European forest because that’s what we had.” The first kangaroo and your model just stares.
And labeling? That’s the tax you pay for supervised learning. Boxes, masks, attributes — hours of human effort per thousand images. One sloppy bounding box and your precision quietly slides.
Why convolutions still feel right (often)
Convs are like good habits: small, local, repeated. They don’t try to understand the whole image at once; they build up from edges → corners → parts → things. That bias is a feature, not a bug — especially when you care about latency , memory , and predictability .
Here’s the tiny formula that defines them:
A classic edge-detection kernel:
That’s it — multiply local pixels by a small pattern and sum them up. Stack enough of those and you’ve got most of early-stage computer vision. It’s beautifully dumb, and it works.
If you’re shipping to a small CPU budget or an edge GPU, a well-tuned CNN still feels like cheating: low latency, stable memory, and mature deployment paths (quantization, compilers, kernels). For many “bread-and-butter” tasks, that’s all you need.
What Transformers change
Transformers don’t start local — they look everywhere at once. That’s amazing when the task is global: documents, long-range structures in medical scans, remote sensing, video. They also scale beautifully with data and pretraining. If you can borrow a big backbone, they transfer like champs.
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flowchart LR
subgraph Perception["Perception (local patterns)"]
I[Image] -->|"Patchify / Conv stem"| T((Tokens))
end
subgraph Reasoning["Reasoning (global)"]
T --> E["Encoder Block × N"]
E --> H["Task Head"]
end
H --> Y[Predictions]
classDef core fill:#1e293b,stroke:#64748b,color:#e5e7eb,rx:6,ry:6
class I,T,E,H,Y core
linkStyle default stroke-width:2
Do they have downsides? Of course. They like data (or big pretraining), and they ask more from your hardware. If your latency budget is “as low as a coffee machine microcontroller,” you’ll feel it.
Why CNNs didn’t stay on top
CNNs didn’t “lose”; they just hit their ceiling on certain problems.
- Global reasoning is hard with only local filters. Receptive fields grow with depth, but long-range dependencies remain clunky to learn.
- Scaling by depth/width has diminishing returns. Beyond a point, you pay a lot of FLOPs for a tiny bump.
- Transfer learning plateaus. A CNN pretrained on ImageNet helps, but if your downstream task diverges (documents, aerial, medical), the boost flattens.
- Transformers unlocked data at scale. Self-attention + massive pretraining (and often multimodal corpora) made generalization easier across domains.
Short version: CNNs are specialists with strong inductive bias; Transformers are generalists that see the whole picture.
Hybrids: the middle path that actually ships
Pure attention everywhere isn’t mandatory. A lot of modern vision backbones blend ideas:
- Conv stem + attention blocks. Keep locality up front, reason globally later.
- Windowed or hierarchical attention. Act local-mostly, global-when-needed.
- CNNs with “Transformer tricks”. Layer norms, large kernels, better training schedules.
Why hybrids win in practice
- Better latency than pure ViT on edge, better accuracy than pure CNN on global tasks.
- Easier training stability than early ViTs, simpler deployment than monster backbones.
- Plays nicely with quantization and existing inference runtimes.
If you like names, think families like Swin , ConvNeXt , CoAtNet , and other conv–attention mashups. Labels aside, the recipe is the same: local bias + global context .
Performance & efficiency (the pragmatic bit)
Attention loves tokens; cost grows with them.
- Convolution : cost grows roughly with image size and kernel size; predictable memory; highly optimized kernels.
- Self-attention : quadratic in tokens for vanilla attention; great global context, but watch your latency and RAM .
Edge device cheat sheet
| Concern | CNN | Transformer |
|---|---|---|
| Latency (CPU) | ✅ Usually lower | ⚠️ Often higher without tricks |
| Memory spikes | ✅ Stable | ⚠️ Can spike with tokens |
| Quantization | ✅ Mature toolchains | ↔️ Getting better, still picky |
| Small datasets | ✅ Strong inductive bias helps | ⚠️ Prefers pretraining |
| Global context | ↔️ Limited without tricks | ✅ Native strength |
When a small Transformer is fine: you have a GPU budget, work with documents/remote sensing, or can reuse a strong pretrained backbone.
When a CNN/hybrid is smarter: tight latency/power, tiny datasets, or you need predictable memory and easy deployment.
A tiny “see it” moment
Edge detection is one of the first things a CNN learns. Even a simple Sobel filter will highlight contours — proof that local structure still tells a story. Transformers may reason globally, but that first act of perception still starts with patterns and contrast.
That’s why inductive biases matter: a CNN can be really good at noticing consistent local cues, fast.
Multimodality (where Transformers really flex)
Pair images with text, audio, or telemetry streams and Transformers feel at home. Learning a shared space where “this picture ↔ that caption” makes sense unlocks zero-shot classification, search, and all sorts of clever tricks. CNNs can participate (hybrids!), but the global reasoning and token-based nature of Transformers make it feel effortless.
“Okay, what should I use?”
- Tight latency / small device / small labeled data → Start with a CNN or a hybrid conv-stem .
- Global context / long-range structure / large pretraining available → Reach for a Transformer .
- Unclear? Try a small hybrid first (conv stem + attention blocks). It’s a solid Pareto move.
And remember: architecture choice rarely saves a broken dataset. Fix your sampling, balance your classes, and evaluate like a cynic.
A quick timeline
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"background": "transparent",
"taskTextColor": "#e5e7eb",
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"taskBorderColor": "#64748b",
"milestoneBkgColor": "#1e293b",
"milestoneBorderColor": "#64748b",
"milestoneTextColor": "#e5e7eb"
}}}%%
gantt
title CNNs -> ViTs (landmarks)
dateFormat YYYY
axisFormat %Y
section CNNs
AlexNet :milestone, m1, 2012, 0d
VGG/Inception :milestone, m2, 2014, 0d
ResNet :milestone, m3, 2015, 0d
section Detectors
YOLO/SSD/FRCNN :milestone, m4, 2016, 0d
section Transformers
ViT :milestone, m5, 2020, 0d
Swin/ConvNeXt :milestone, m6, 2021, 0d
Closing thought
CNNs aren’t dead — they’re specialists . Transformers are generalists that scale and mix modalities. If you can pretrain big and need global reasoning, go Transformer. If you’re shipping something scrappy and fast on the edge, a tidy CNN is still the pragmatic win.
Most of the time, the best answer is a small, boring hybrid that respects your budget and your data.