Instructions to use se0kcess/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use se0kcess/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="se0kcess/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("se0kcess/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("se0kcess/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from se0kcess/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/se0kcess/cppe5_use_data_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://se0kcess/cppe5_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/se0kcess/cppe5_use_data_finetuning/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 3db4c6febb9d20189be9552c54a3fbc59d3310820dd7c51b0ac9f92c37cae202
- Size of remote file:
- 4.54 kB
- SHA256:
- 86068a5ae09a443ecb52909e6852d864d4c2ab7d9fbe9c8744c25ad67dc877f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.