Instructions to use facebook/regnet-y-640-seer-in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-640-seer-in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-640-seer-in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-640-seer-in1k") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-640-seer-in1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c78a516ece838707d0139e1d3630e53a8462a6c1ac25a13ce499b8c9cf86e96
- Size of remote file:
- 1.13 GB
- SHA256:
- be77ba562e1b5081a8ab34c768dfe7775f5a6e6704e9f45949a5ffc5f315ed45
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