Instructions to use taohungchang/candy_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taohungchang/candy_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="taohungchang/candy_model")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("taohungchang/candy_model") model = AutoModelForObjectDetection.from_pretrained("taohungchang/candy_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 046482e59ac21557e2a86beeabb2ea9b23ce060371bcdf7de0582d61675c4b05
- Size of remote file:
- 167 MB
- SHA256:
- 65255c9a969e7a479ced260444f064f8b4b3a2d65f32ef20c975c7f93e23b222
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.