Conv2D FLOPs Calculator
Multiply-adds of a convolutional layer over a full feature map — see why early layers dominate compute.
Defaults are a ResNet-50 stage-1 block conv: 3×3, 64→64 at 56×56 ≈ 0.46 GFLOPs. Note how compute scales with the feature-map area — early high-resolution layers burn most of a CNN's budget even with few channels.
Formula
About Conv2D FLOPs Calculator
Parameters tell you a CNN's download size; FLOPs tell you its speed — and the two diverge wildly for convolutions because every weight is reused across the whole feature map. This calculator multiplies the kernel volume by the output area to give exact multiply-accumulate and FLOP counts per layer. It makes the classic insight tangible: a 3×3 on 64 channels at 56×56 costs as much as a much fatter layer at 7×7, which is why architectures downsample aggressively early and why 'efficient' backbones obsess over the first two stages.
How to use Conv2D FLOPs Calculator
- 1Enter your values into Conv2D FLOPs Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Conv2D FLOPs Calculator?
- ✓Computes Conv2D FLOPs instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: FLOPs = 2 × k² × Cin × Cout × Hout × Wout (×2 converts multiply-accumulates to FLOPs).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
MACs vs FLOPs — which do papers report?+
Inconsistently, infuriatingly. One MAC = one multiply + one add = 2 FLOPs, but many vision papers write 'FLOPs' while reporting MACs. ResNet-50 is 4.1 GMACs = 8.2 GFLOPs; if a paper says ResNet-50 = 4.1 GFLOPs, they mean MACs. This tool reports both, labeled.
Why do early layers dominate CNN compute?+
FLOPs scale with Hout×Wout. At 112×112, one pixel of output costs the same kernel volume as at 7×7 — but there are 256× more pixels. Even with 8× fewer channels, stage-1 layers usually out-cost the deep, wide, tiny-map layers at the end.
Do FLOPs predict real latency?+
Only roughly. Memory layout, channel counts hitting tensor-core tile sizes, activation functions and memory-bound depthwise layers all break the correlation — MobileNet has few FLOPs but its depthwise convs underuse GPUs. Treat FLOPs as a first-order budget, then profile.
How do I get a whole model's GFLOPs?+
Sum this formula over layers — or use a profiler (torchinfo, fvcore, ptflops) that does it for you. This calculator is for the design loop: checking how a proposed stride, width or kernel change moves a single stage before you write any code.
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