Instructions to use AmineAllo/MT-swept-armadillo-86 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/MT-swept-armadillo-86 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/MT-swept-armadillo-86")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/MT-swept-armadillo-86") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/MT-swept-armadillo-86", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AmineAllo/MT-swept-armadillo-86: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/AmineAllo/MT-swept-armadillo-86/resolve/main/training_args.bin
- Command line
-
hf download hf://AmineAllo/MT-swept-armadillo-86/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AmineAllo/MT-swept-armadillo-86/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- bb72c7612c579253024d43a43f0c5f92693d29c11167e94bfff06c0226d481ed
- Size of remote file:
- 4.47 kB
- SHA256:
- 7f806b194ee4881083219d9b822becc26d68f9e150aba8019cb3f1db984e662c
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