Instructions to use timm/lambda_resnet26rpt_256.c1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/lambda_resnet26rpt_256.c1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/lambda_resnet26rpt_256.c1_in1k", pretrained=True) - Transformers
How to use timm/lambda_resnet26rpt_256.c1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/lambda_resnet26rpt_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/lambda_resnet26rpt_256.c1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a2a9499d139106b0a65ef234529533705e7c589c35566e15b53f3645c14c82c
- Size of remote file:
- 44.2 MB
- SHA256:
- eb9e7eef13e53df41f694144039aaaf8ecf76c82249056158d8851f5be94fed7
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