Instructions to use swjin/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swjin/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="swjin/cppe5_use_data_finetuning")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("swjin/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("swjin/cppe5_use_data_finetuning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05db967a2e4789c2aac0019ee847b77d4d45bc36aeb65813b3ece64ea22dc94e
- Size of remote file:
- 166 MB
- SHA256:
- 723246dfc10da1a6db4eb01ebd784ee11112e40fd371cf99b9560064902c93b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.