Instructions to use noori4543/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noori4543/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="noori4543/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("noori4543/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("noori4543/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from noori4543/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/noori4543/cppe5_use_data_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://noori4543/cppe5_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/noori4543/cppe5_use_data_finetuning/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 4dab1af3f990bc6669d1a3764dbf0a9b0a7366a4d18a6983cfeea2e64ed21686
- Size of remote file:
- 4.54 kB
- SHA256:
- 133862cb31195c8b59c5980f17ddb3ef2a5d4ef7d499589d9ff62fa2d8d13876
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.