Instructions to use urbanManul/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use urbanManul/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="urbanManul/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("urbanManul/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("urbanManul/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from urbanManul/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/urbanManul/cppe5_use_data_finetuning/resolve/206a2f8d35fc6994f82fce3ec22ff90eb00a483a/pytorch_model.bin
- Command line
-
hf download hf://urbanManul/cppe5_use_data_finetuning@206a2f8d35fc6994f82fce3ec22ff90eb00a483a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/urbanManul/cppe5_use_data_finetuning/resolve/206a2f8d35fc6994f82fce3ec22ff90eb00a483a/pytorch_model.bin
167 MB
- Xet hash:
- d2c4fc32e8813010989eb5f8b41177be659942179f330ca205d3989dcbcf2191
- Size of remote file:
- 167 MB
- SHA256:
- 08e742c478146f077e7536073cfb3b9d5535abc391449614b63918ad54b295ad
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