Instructions to use OhST/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OhST/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="OhST/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("OhST/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("OhST/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OhST/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/OhST/cppe5_use_data_finetuning/resolve/eef8ab10ef6b8060d920dc83aa070fcd8dd8d852/pytorch_model.bin
- Command line
-
hf download hf://OhST/cppe5_use_data_finetuning@eef8ab10ef6b8060d920dc83aa070fcd8dd8d852/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OhST/cppe5_use_data_finetuning/resolve/eef8ab10ef6b8060d920dc83aa070fcd8dd8d852/pytorch_model.bin
167 MB
- Xet hash:
- 7a9c098c2ac8ae85ac1b385699251ceee2cea6e898e5680e86182dfd88196bec
- Size of remote file:
- 167 MB
- SHA256:
- 720382355fc4cb161b0ef0c131dc3a99e8edc5ebe839ee7c69abfc580a92caf8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.