Instructions to use panda47/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use panda47/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="panda47/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("panda47/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("panda47/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from panda47/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/panda47/cppe5_use_data_finetuning/resolve/ef17473154b45c71ee00a73219e5645bcf873136/training_args.bin
- Command line
-
hf download hf://panda47/cppe5_use_data_finetuning@ef17473154b45c71ee00a73219e5645bcf873136/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/panda47/cppe5_use_data_finetuning/resolve/ef17473154b45c71ee00a73219e5645bcf873136/training_args.bin
4.54 kB
- Xet hash:
- e89fe9a8b7398032cc65b5d241f492ac9701d775046c000b779bc9ba36288ccc
- Size of remote file:
- 4.54 kB
- SHA256:
- 22af535360b101829386abdf820ced6904149dc1e5a714717032218423bee4fb
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