Instructions to use fimbit/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fimbit/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="fimbit/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("fimbit/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("fimbit/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fimbit/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/fimbit/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://fimbit/detr-resnet-50_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fimbit/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 0d41ea84bf4468e8369bed837303c5e7c500dcef81bd0176a607c9bbd068eea9
- Size of remote file:
- 5.18 kB
- SHA256:
- 25c719fd9a409dca3cfe997a11cdb9b3a96d43a77e43bffd388d1c6d7faa2410
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