Instructions to use Rareshika/yolos_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rareshika/yolos_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Rareshika/yolos_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Rareshika/yolos_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("Rareshika/yolos_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Rareshika/yolos_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Rareshika/yolos_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://Rareshika/yolos_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Rareshika/yolos_finetuned_cppe5/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- f464e0d689026fe50d752349ec23edce7277a41e83686167f304b80a9ddf2c3b
- Size of remote file:
- 5.11 kB
- SHA256:
- fd027f542b1d6a9c70032e9b348aec2b6be1012e4da5deb83eeced5e1904e0f5
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