metadata
library_name: timm
license: apache-2.0
pipeline_tag: image-classification
tags:
- timm
- image-classification
datasets:
- imagenet-1k
CPUBone
ImageNet-1k classification weights for the CPUBone model family (CVPR 2025), converted to the
timm implementation of the architecture.
All checkpoints here are the final EMA weights (decay 0.9998) from the original training runs,
converted to safetensors in the timm state-dict layout.
Variants
| Weights | Params | ImageNet top-1 | ImageNet top-5 |
|---|---|---|---|
cpubone_nano.safetensors |
6.5M | 72.80 | 90.63 |
cpubone_b0.safetensors |
10.4M | 77.40 | 93.57 |
cpubone_b1.safetensors |
12.4M | 78.54 | 94.05 |
cpubone_b2.safetensors |
30.4M | 81.20 | 95.45 |
cpubone_b3.safetensors |
40.7M | 83.03 | 96.37 |
Accuracies were measured on the ImageNet-1k validation split with the timm implementation and
its default eval preprocessing for these weights (224x224, bicubic, crop_pct 0.95), and match the
original training logs within ±0.16pp.
Usage
Requires a timm version that includes the cpubone models:
import timm
model = timm.create_model("cpubone_b0", pretrained=True).eval()