Instructions to use timm/regnetx_006.pycls_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnetx_006.pycls_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnetx_006.pycls_in1k", pretrained=True) - Transformers
How to use timm/regnetx_006.pycls_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnetx_006.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_006.pycls_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e150f3e40c0f57b752fd77d198c575096f5e1c640565e39d21b5d8f0bbb8ee04
- Size of remote file:
- 25 MB
- SHA256:
- 5423146b323d906113381f4214b2c7965b299562d3ba431b8129f0598ae97151
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