Instructions to use Moonjunho/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moonjunho/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Moonjunho/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Moonjunho/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("Moonjunho/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Moonjunho/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Moonjunho/cppe5_use_data_finetuning/resolve/32637bebb5d883c3ca01dc6a8ea7b00bfa7b829c/pytorch_model.bin
- Command line
-
hf download hf://Moonjunho/cppe5_use_data_finetuning@32637bebb5d883c3ca01dc6a8ea7b00bfa7b829c/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Moonjunho/cppe5_use_data_finetuning/resolve/32637bebb5d883c3ca01dc6a8ea7b00bfa7b829c/pytorch_model.bin
167 MB
- Xet hash:
- a31e496ec3cede1446766c10580f66fbe7342d0b5693415d0bdef2065e4651b0
- Size of remote file:
- 167 MB
- SHA256:
- 3ccc77788f2460a9f1d41c5679d081d81e755e51a9d3324e7b6f2a3b54aafa57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.