Instructions to use timm/resnetrs200.tf_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnetrs200.tf_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnetrs200.tf_in1k", pretrained=True) - Transformers
How to use timm/resnetrs200.tf_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnetrs200.tf_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/resnetrs200.tf_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f59ebfc4da7a07252a30cfcef82464e0473826371e43db5d68e7e3bad8b562c
- Size of remote file:
- 374 MB
- SHA256:
- 224275ae627fd546d0f149491f0af70aac81bc4896a1ae68963dac8c58c923d3
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