Instructions to use CristianR8/efficientnet-b0-cocoa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CristianR8/efficientnet-b0-cocoa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CristianR8/efficientnet-b0-cocoa") 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("CristianR8/efficientnet-b0-cocoa") model = AutoModelForImageClassification.from_pretrained("CristianR8/efficientnet-b0-cocoa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2130a982a2f8081b786261afe6e69effc62f7643edbc39973b3067bfad42853
- Size of remote file:
- 5.43 kB
- SHA256:
- 12d5b23b940d30ac536afa0ff8128a601e80842f0102c51db69e8e33cfef8e39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.