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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:
```python
import timm
model = timm.create_model("cpubone_b0", pretrained=True).eval()
```
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