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 pytorch_model.bin from aaa950739/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/aaa950739/trained_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://aaa950739/trained_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aaa950739/trained_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
167 MB
- Xet hash:
- 428b30072a635af4ac0908fa5abeb0160e853d9f21611485b78548e5c8816791
- Size of remote file:
- 167 MB
- SHA256:
- f4c27c8d40b9938cbcb03978ab4afe14efa8d1eb6279fff3cab9f3c5dde354bd
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