Instructions to use timm/resnet34.bt_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet34.bt_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet34.bt_in1k", pretrained=True) - Transformers
How to use timm/resnet34.bt_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet34.bt_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/resnet34.bt_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11af472eea566e7e149a6bcf625a396b1813bfe02aeb80bbc8d729dad3169fcb
- Size of remote file:
- 87.3 MB
- SHA256:
- 0fd357be72132e3527c6d88a54a19a22e597470e6ea9ac536a56ac3baf1e5a00
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