| 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() | |
| ``` | |