Feature Extraction
Transformers
PyTorch
English
transformer
variational-autoencoder
bev
autonomous-driving
Instructions to use czm369/BEV-VAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use czm369/BEV-VAE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="czm369/BEV-VAE")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("czm369/BEV-VAE", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 21fb4b41a154b5bd4882af60733839216cb8a9471d9bdcfcb2b22243e1b4f7c1
- Size of remote file:
- 630 kB
- SHA256:
- 82cad0db22005bf26140943541dc607423e7b78712bdab9bae3756968b5030c2
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