Image Classification
timm
Russian
English
manuscript
bookbinding
cultural-heritage
digital-humanities
convnext
fine-tuned
Instructions to use Infarondus/Klein-marchen_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Infarondus/Klein-marchen_Base with timm:
import timm model = timm.create_model("hf_hub:Infarondus/Klein-marchen_Base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- daf66f688789367b8f46069fbd17350c881187da78d81f1576dc2cbf2834bf7b
- Size of remote file:
- 87.2 MB
- SHA256:
- 9a4465cc03d48d64c23cab3fb1afcf0c5c150efc4eac641936fe3fa18d9d0612
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.