Instructions to use lillythomas/rtdetr-v2-r50-cppe5-finetune-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lillythomas/rtdetr-v2-r50-cppe5-finetune-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="lillythomas/rtdetr-v2-r50-cppe5-finetune-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("lillythomas/rtdetr-v2-r50-cppe5-finetune-2") model = AutoModelForObjectDetection.from_pretrained("lillythomas/rtdetr-v2-r50-cppe5-finetune-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lillythomas/rtdetr-v2-r50-cppe5-finetune-2: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/lillythomas/rtdetr-v2-r50-cppe5-finetune-2/resolve/main/training_args.bin
- Command line
-
hf download hf://lillythomas/rtdetr-v2-r50-cppe5-finetune-2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lillythomas/rtdetr-v2-r50-cppe5-finetune-2/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- d0c373ecf1716f52e3f70a2604d6452a56a7f9cc8b9c738ce717b3a276f863a1
- Size of remote file:
- 5.37 kB
- SHA256:
- 260b5be58396f9e90d42898fc0657884dab08407e1ffcbf17676a4d09048dfa2
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