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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("czm369/BEV-VAE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pt_inception-2015-12-05-6726825d.pth from czm369/BEV-VAE: direct link, hf CLI and curl.
- Browser
- Download file 95.6 MB
-
https://huggingface.co/czm369/BEV-VAE/resolve/main/pt_inception-2015-12-05-6726825d.pth
- Command line
-
hf download hf://czm369/BEV-VAE/pt_inception-2015-12-05-6726825d.pth
-
curl -L -o pt_inception-2015-12-05-6726825d.pth https://huggingface.co/czm369/BEV-VAE/resolve/main/pt_inception-2015-12-05-6726825d.pth
95.6 MB
- Xet hash:
- 0f93d1fdffa88acc791ba16625c085ec6e0bd2374fafd7f64d1e8a3236f4c4ef
- Size of remote file:
- 95.6 MB
- SHA256:
- 6726825d0af5f729cebd5821db510b11b1cfad8faad88a03f1befd49fb9129b2
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