Instructions to use cmncomp/roadwork with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmncomp/roadwork with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cmncomp/roadwork") 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("cmncomp/roadwork") model = AutoModelForImageClassification.from_pretrained("cmncomp/roadwork") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3eb02d643c0f570457f4ea03f10af3f6443cb35fd920b0816f64d9e131dca250
- Size of remote file:
- 343 MB
- SHA256:
- 56a376863c5b2cb7201fe3537625c115bbe163b600185bb62dcd4772f13b6d38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.