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 pytorch_model.bin from AmineAllo/MT-swept-armadillo-86: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/AmineAllo/MT-swept-armadillo-86/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AmineAllo/MT-swept-armadillo-86/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AmineAllo/MT-swept-armadillo-86/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- cafd669e2f71161f1dae3d5868d9f5a0c5654012b6121c713793bf70e630e15d
- Size of remote file:
- 115 MB
- SHA256:
- 112aab0c5f551e617eb315f058579bc36f5b6ceb9deeb8610ceda6ccda2c4a22
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