Instructions to use cppgohan/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cppgohan/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cppgohan/my_model") 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("cppgohan/my_model") model = AutoModelForImageClassification.from_pretrained("cppgohan/my_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fe2b57aa3935dd0ec2ec94908b100e06f2b7abe76a192331f415cc55a7648b7
- Size of remote file:
- 4.86 kB
- SHA256:
- 84fb1d7082955c346ff8a1455559ec792352d13e67f414b657758d59aacb450b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.