Instructions to use aaa950739/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaa950739/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="aaa950739/trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("aaa950739/trained_model") model = AutoModelForObjectDetection.from_pretrained("aaa950739/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aaa950739/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/aaa950739/trained_model/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://aaa950739/trained_model@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aaa950739/trained_model/resolve/refs%2Fpr%2F1/training_args.bin
3.9 kB
- Xet hash:
- 6c209f001fa7d17a9e01338faa78fa46058e426e50b0756e001453a1ae541c1c
- Size of remote file:
- 3.9 kB
- SHA256:
- 136fb482f4109a9409b8d3b9a53d6d7b48559cd176b18763f2dd6923614056af
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