Instructions to use taohungchang/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taohungchang/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="taohungchang/trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("taohungchang/trained_model") model = AutoModelForObjectDetection.from_pretrained("taohungchang/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from taohungchang/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/taohungchang/trained_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://taohungchang/trained_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/taohungchang/trained_model/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 8e84aee6297233a8610e63de236e73c54424f4d566b85ad6ef3c81e4627daf85
- Size of remote file:
- 167 MB
- SHA256:
- 4c8426f519a070e1d684a7d772d119593acf27dda8674b50ddcfcac02a82593f
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