Instructions to use timm/resnet152.a1h_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet152.a1h_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet152.a1h_in1k", pretrained=True) - Transformers
How to use timm/resnet152.a1h_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet152.a1h_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/resnet152.a1h_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9ab951bc09988fd3e03a7f73c88c6bac84b8a8cb6d14a9f5cf8063ea8673621
- Size of remote file:
- 242 MB
- SHA256:
- 7508543fdd2d6670e0d14c951d4a83959687ef95f83efd0d1eaa4e4a9f628c4c
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