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 pytorch_model.bin from Yoon310/furniture_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Yoon310/furniture_use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Yoon310/furniture_use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Yoon310/furniture_use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 85d416b9bb10fd2e2267933a5ee52d2dd77f267bbdf017a52f9523f34cc655b8
- Size of remote file:
- 167 MB
- SHA256:
- ff8b1192990e45a2a1998a692f6cba6552fabce153551bea56bf705a0d571616
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