Instructions to use timm/botnet26t_256.c1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/botnet26t_256.c1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/botnet26t_256.c1_in1k", pretrained=True) - Transformers
How to use timm/botnet26t_256.c1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/botnet26t_256.c1_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/botnet26t_256.c1_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c128fd810d837434e15881668112285bb0d52d917ef87a32241d7925846e4d6b
- Size of remote file:
- 50.1 MB
- SHA256:
- 74c5d40061bfb29e692bc96f5ea45fa623b08bf4881d7e11e7bc8069426c17b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.