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/main/training_args.bin
- Command line
-
hf download hf://swjin/cppe5_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 857c0f2c516bd225b3b3e6d6e919cbcfe9be01c6e2129f2d4dc8705cc58a0769
- Size of remote file:
- 4.54 kB
- SHA256:
- 42a01e662f9f9655f4a3cd8a40199aea1c6b5479129ca7ae75814ab192796967
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