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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("taohungchang/candy_model") model = AutoModelForObjectDetection.from_pretrained("taohungchang/candy_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from taohungchang/candy_model: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/taohungchang/candy_model/resolve/main/training_args.bin
- Command line
-
hf download hf://taohungchang/candy_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/taohungchang/candy_model/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 8bb0d2cf72680e0c1242553c01b50a2cc92ac55d9a9c911a3dd255cc8280b873
- Size of remote file:
- 3.9 kB
- SHA256:
- d14e867b2668b4ac44c1f956154972537e5448ed58266436fb8cd46bb45d58c7
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