Instructions to use James332/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use James332/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="James332/cppe5_use_data_finetuning")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("James332/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("James332/cppe5_use_data_finetuning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 676a754529684ee27fa0ff6735f00b6c1efcd84f98b590c1242115ca7c092a61
- Size of remote file:
- 4.54 kB
- SHA256:
- 8858cbd993a1439ac5906d1f13d69a5960fa51fb84df802041d734826d732961
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.