Instructions to use flatmoon102/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flatmoon102/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="flatmoon102/image_classification") 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("flatmoon102/image_classification") model = AutoModelForImageClassification.from_pretrained("flatmoon102/image_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cad00660fa5d5decf5da22173576ad230d71d90f8e652b2901b72e91513e22bc
- Size of remote file:
- 4.09 kB
- SHA256:
- 306b87d9ba5f723e9d1b6f882d7d0d77790f6cc99b81c3aa3a29e36bbd8fc0ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.