Instructions to use mwalmsley/baseline-encoder-regression-maxvit_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/baseline-encoder-regression-maxvit_tiny with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-regression-maxvit_tiny", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc2146575c13da62bc45142a1ab9df21b481586915693ff4ff65083b5d500d0a
- Size of remote file:
- 115 MB
- SHA256:
- bc0572cdb5600b881586d39837d4eb4f3d990024a361d3cc2fd8c2f0e8df80ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.