Instructions to use softdev629/gm2jdlzu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softdev629/gm2jdlzu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="softdev629/gm2jdlzu") 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("softdev629/gm2jdlzu") model = AutoModelForImageClassification.from_pretrained("softdev629/gm2jdlzu") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "roadwork-snapshot-gm2jdlzu", | |
| "description": "Snapshot model from natix-network-org/roadwork", | |
| "version": "1.0.0", | |
| "submitted_by": "5Di9mgRQ5qLRf25kPnfX7oFeSEQ82PJtVn2HKen8W1zm8Xt7", | |
| "submission_time": 1748993676 | |
| } |