Instructions to use D-Roberts/tf-efficientformer-l1-300-dev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use D-Roberts/tf-efficientformer-l1-300-dev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="D-Roberts/tf-efficientformer-l1-300-dev2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("D-Roberts/tf-efficientformer-l1-300-dev2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b478ad46e81e5dccf8f9907fca6ab8389b969ef4bbccfbdef22169e4926f305
- Size of remote file:
- 45.9 MB
- SHA256:
- ad3867187f54a70e73cee85e58f1b7b920adf26b5d6a945eac3b863567e4ed92
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.