Instructions to use jamjammin/use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamjammin/use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jamjammin/use_data_finetuning")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("jamjammin/use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("jamjammin/use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf4e6da5e664dcf395ccff38648f90cf2293d207ef78abe0e08c15e74df79a45
- Size of remote file:
- 166 MB
- SHA256:
- 584900dd812ef1beab475a807d4747d6f906e51942aa2ca27e3325990676b93c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.