Instructions to use AmineAllo/margin-element-detector-fm-pretty-wind-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/margin-element-detector-fm-pretty-wind-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/margin-element-detector-fm-pretty-wind-5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/margin-element-detector-fm-pretty-wind-5") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/margin-element-detector-fm-pretty-wind-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AmineAllo/margin-element-detector-fm-pretty-wind-5: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/AmineAllo/margin-element-detector-fm-pretty-wind-5/resolve/main/training_args.bin
- Command line
-
hf download hf://AmineAllo/margin-element-detector-fm-pretty-wind-5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AmineAllo/margin-element-detector-fm-pretty-wind-5/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- 1818b95aa5994756b8786ef20162e54285507cd5afcfffbd0b146a3e80519348
- Size of remote file:
- 4.47 kB
- SHA256:
- ab866d0253b56686fbaec1aa38053e758335c5a868dc41453a21b7e5a92f86f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.