Instructions to use tdunlap607/vfc-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdunlap607/vfc-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="tdunlap607/vfc-identification", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tdunlap607/vfc-identification", trust_remote_code=True) model = AutoModel.from_pretrained("tdunlap607/vfc-identification", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a50e8f65179c2f673d21ffc3ca03da406a18b7f0314f7245dc711ec0ab238ee
- Size of remote file:
- 499 MB
- SHA256:
- ea8a45a5fe39d50aa4758cfff7aef00d9e7baae698229a1adbca8bd00b2a9aaa
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