Instructions to use aguxez/snap-plate-stack-instance-dfine-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aguxez/snap-plate-stack-instance-dfine-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="aguxez/snap-plate-stack-instance-dfine-v5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("aguxez/snap-plate-stack-instance-dfine-v5") model = AutoModelForObjectDetection.from_pretrained("aguxez/snap-plate-stack-instance-dfine-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aguxez/snap-plate-stack-instance-dfine-v5: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/aguxez/snap-plate-stack-instance-dfine-v5/resolve/main/training_args.bin
- Command line
-
hf download hf://aguxez/snap-plate-stack-instance-dfine-v5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aguxez/snap-plate-stack-instance-dfine-v5/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- 477570f8514a162dc2479d0d8c41dd1ff09435cac9e29174339fe4efb287f1cc
- Size of remote file:
- 5.33 kB
- SHA256:
- 96d0581b940618906f22db1ac853c47ecb886eae83b8227fddf75972b1df939d
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