jcjohnson/cnn-benchmarks resource
Benchmarks for popular CNN models observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3703
- days_rel: n/a
- days_push: 3264
- n_releases_24m: 0
Adoption not part of the score
2532 stars · 403 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of benchmark results and scripts measuring inference speed of popular CNN models (AlexNet, VGG, ResNet, Inception) across CPUs and various NVIDIA GPUs, with and without cuDNN, using Torch. It includes accuracy and latency tables to help compare hardware and model choices for deep learning.
Use cases
- compare GPU speed for CNN inference
- decide between GTX 1080 and Titan X for deep learning
- benchmark ResNet vs VGG latency
- measure cuDNN speedup over nn
- find fastest CNN model for my GPU
- evaluate CPU vs GPU training performance
When to choose
- comparing modern GPUs or frameworks
- you need actively maintained benchmarks
- you work with PyTorch or TensorFlow rather than Torch
Facets
dataset · maturity abandoned
benchmarking machine-learning deep-learning deep-learning gpu-computing performance computer-vision python cnn torch cudnn gpu-benchmarks resnet vgg alexnet inference-speed linux gpu
1 source
- readme: https://github.com/jcjohnson/cnn-benchmarks · fetched 2026-08-28 · 0991db09a5a2
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| jcjohnson/cnn-benchmarks | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="jcjohnson/cnn-benchmarks")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem