Instructions to use timm/regnetx_080.pycls_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnetx_080.pycls_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnetx_080.pycls_in1k", pretrained=True) - Transformers
How to use timm/regnetx_080.pycls_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnetx_080.pycls_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/regnetx_080.pycls_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4620032ccfb14bd7bd69ed11febcdb1d890bb52b512572f4ac37d04e57d2fd4d
- Size of remote file:
- 159 MB
- SHA256:
- 1e1801d6f4e119ec1f96d37f0992ae876e66e0243d1a77042dfd61638a5d89bb
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