Instructions to use swjin/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swjin/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="swjin/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("swjin/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("swjin/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from swjin/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/training_args.bin
- Command line
-
hf download hf://swjin/cppe5_use_data_finetuning@8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/training_args.bin
4.54 kB
- Xet hash:
- 80be39ed4d4ec2243167f72047fd9cf55787ec74773cdd14fae51a3169565301
- Size of remote file:
- 4.54 kB
- SHA256:
- c8078f93c063851ff5f6ae9e7ff138906cfe2275b8428268aa0c6db0fc6f7706
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