Instructions to use Yoon310/furniture_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yoon310/furniture_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Yoon310/furniture_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Yoon310/furniture_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("Yoon310/furniture_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Yoon310/furniture_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/Yoon310/furniture_use_data_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://Yoon310/furniture_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Yoon310/furniture_use_data_finetuning/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- e9f8833edaa02f556747c719b16515d9205ad0208873b21392e53224f585810b
- Size of remote file:
- 4.54 kB
- SHA256:
- 1bc0bc2378283d593f3e117753d25cc68793700e7ba8e09d88f05ac9d4d8af17
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.