Instructions to use Manasee27/cvmod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manasee27/cvmod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Manasee27/cvmod") 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("Manasee27/cvmod") model = AutoModelForImageClassification.from_pretrained("Manasee27/cvmod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8feba6dce40c129222f50249695391a99cc72eda4816acd40a137206144226f7
- Size of remote file:
- 343 MB
- SHA256:
- 5fede4564407f32965d9be18d416758859a11c68a8a3d44c4b2d562da48adb69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.