CPUBone: Efficient Vision Backbone Design for Devices with Low Parallelization Capabilities

Paper (CVPR) | Paper (arXiv) | CVPR Findings 2026

Official repository for CPUBone, a family of vision backbones optimized for CPU-based inference.

Authors: Moritz Nottebaum, Matteo Dunnhofer and Christian Micheloni

Model checkpoints are available now!! here (see Quick Start for more info).

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Overview

CPUs, unlike GPUs and other high-parallelization platforms, require models that balance the number of operations (MACs) and hardware-efficient execution measured by MACs per second (MACpS). CPUBone investigates two modifications to standard convolutions — grouped convolutions (groups=2) and reduced kernel sizes (2×2 instead of 3×3) — that reduce MACs while preserving MACpS on CPUs, achieving state-of-the-art speed–accuracy trade-offs across a wide range of devices.

CPUBone also transfers its efficiency to downstream tasks (object detection, semantic segmentation).

If you are looking for a more general-purpose backbone (not CPU-specific), our LowFormer may be more suitable.

Latency benchmarks are run on exported models: desktop and embedded CPU numbers (RPi 5, Xeon) use ONNX Runtime, while mobile numbers (Pixel 7 Pro) use TFLite.

The full model definition is self-contained in cpubone_model.py and requires only PyTorch.

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Model Zoo

ImageNet-1K Classification

Model	Params (M)	MACs (M)	Top-1 (%)	RPi 5 (ms)	Pixel 7 Pro (ms)	Xeon W-2125 (ms)
CPUBone-B0	10.4	519	77.6	24.2	13.8	6.9
CPUBone-B1	12.4	746	78.7	33.5	18.6	9.4
CPUBone-B2	23.9	1354	80.3	60.3	32.4	16.1
CPUBone-B3	40.7	4054	83.1	199.8	83.1	34.1

Models and ablations can be found here (just put the folder in the root directory). Latency measured at batch size 1. RPi 5 and Xeon numbers use ONNX Runtime; Pixel 7 Pro numbers use TFLite.

Tiny Backbones

Model	Params (M)	MACs (M)	Top-1 (%)	RPi 5 (ms)
CPUBone-Nano	6.52	190	72.67	—
CPUBone-T0	7.54	269	74.85	12.47
CPUBone-S0	8.73	359	75.89	15.53

Downstream Tasks

Object Detection on COCO 2017 (RetinaNet framework):

Model	AP (%)
CPUBone-B0	37.5
CPUBone-B1	39.0
CPUBone-B2	40.4
CPUBone-B3	42.9

Semantic Segmentation on ADE20K (Semantic FPN framework):

Model	mIoU (%)
CPUBone-B0	37.9
CPUBone-B1	39.2
CPUBone-B2	42.1
CPUBone-B3	44.1

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Quick Start: Using a Pretrained Model

cpubone_model.py is a standalone file — the only dependency is PyTorch.

Model loading requires the YAML config files from the configs/ directory of this repository. Each config defines the architecture hyperparameters (width, depth, etc.) for a given model variant.

from cpubone_model import get_cpubone_b1
import torch

# Requires configs/cls/imagenet/cpubone_b1.yaml to be present
model = get_cpubone_b1(pretrained=True)
model.eval()

x = torch.randn(1, 3, 224, 224)
out = model(x)  # (1, 1000)
print(out.shape)

Available convenience functions: get_cpubone_b0, get_cpubone_b1, get_cpubone_b15, get_cpubone_b2, get_cpubone_b3.

Pretrained weights are expected at .exp/cls/imagenet/cpubone_<name>/checkpoint/evalmodel.pt. You can simply download checkpoint folder and put it into this project directory (might be neccessary to append a period (".") at the beginning to the folder after download). It contains all the models and ablations. Both the config path and checkpoint path can be passed explicitly:

from cpubone_model import get_cpubone

model = get_cpubone(
    config_path="configs/cls/imagenet/cpubone_b1.yaml",
    checkpoint_path="path/to/evalmodel.pt",
    pretrained=True,
)

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Installation

conda create -n cpubone python=3.11
conda activate cpubone
pip install -r requirements.txt

For ImageNet-1K training and evaluation, set the dataset path in the config file (configs\cls\imagenet\default.yml). Checkpoints can be downloaded from here.

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Evaluation

python eval_cls_model.py --model cpubone_b1 --path /path/to/imagenet/val

--path must point to the ImageNet validation directory (the folder containing the class subfolders).

Additional flags:

• --testrun — skip weight loading and dataset, useful for architecture checks
• --latency — measure CPU/GPU latency with TorchScript
• --bench — full throughput benchmark

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Training

Single GPU:

torchrun --nproc_per_node=1 train_cls_model.py \
    configs/cls/imagenet/cpubone_b1.yaml \
    --path .exp/cls/imagenet/cpubone_b1

Multi-GPU (8 GPUs):

torchrun --nproc_per_node=8 train_cls_model.py \
    configs/cls/imagenet/cpubone_b1.yaml \
    --path .exp/cls/imagenet/cpubone_b1

To resume training, add --resume.

Gradient accumulation is controlled via the bsizemult parameter in the run config. Learning rate is scaled automatically with world size when total_lr and total_batch_size are set in the config.

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Custom Models

The easiest way to define a custom CPUBone variant is via the custom model name in a config file:

net_config:
  name: custom
  width_list: [16, 32, 64, 128, 256]
  depth_list: [0, 1, 1, 4, 4]
  fastit: true

Or directly in Python by passing kwargs to cpubone_backbone_b1:

from cpubone_model import cpubone_backbone_b1

backbone, width_list = cpubone_backbone_b1(
    name="custom",
    width_list=[16, 32, 64, 128, 256],
    depth_list=[0, 1, 1, 4, 4],
    fastit=True,
)

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Acknowledgements

The training infrastructure in the cpubone/ package is built on top of EfficientViT. The code in this repository also builds on our prior work LowFormer.

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Citation

@inproceedings{nottebaum2026cpubone,
  title     = {CPUBone: Efficient Vision Backbone Design for Devices with Low Parallelization Capabilities},
  author    = {Nottebaum, Moritz and Dunnhofer, Matteo and Micheloni, Christian},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2026}
}