Instructions to use AmineAllo/margin-element-detector-fm-dutiful-morning-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/margin-element-detector-fm-dutiful-morning-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/margin-element-detector-fm-dutiful-morning-4")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/margin-element-detector-fm-dutiful-morning-4") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/margin-element-detector-fm-dutiful-morning-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AmineAllo/margin-element-detector-fm-dutiful-morning-4: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/AmineAllo/margin-element-detector-fm-dutiful-morning-4/resolve/main/training_args.bin
- Command line
-
hf download hf://AmineAllo/margin-element-detector-fm-dutiful-morning-4/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/AmineAllo/margin-element-detector-fm-dutiful-morning-4/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 55ca43dad699ea726535662977f1b3d45a3ba2b3016050e18368b249765b8b3e
- Size of remote file:
- 4.54 kB
- SHA256:
- 68a0df651d2d7c581b186945ba89a814c5ad7847225f2602bd7d4c8f12b58d97
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