Instructions to use EverJun2/MLweek7_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EverJun2/MLweek7_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="EverJun2/MLweek7_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("EverJun2/MLweek7_1") model = AutoModelForObjectDetection.from_pretrained("EverJun2/MLweek7_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EverJun2/MLweek7_1: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/EverJun2/MLweek7_1/resolve/eebd94deab4f9129a6d730c832167b6dce216fa6/training_args.bin
- Command line
-
hf download hf://EverJun2/MLweek7_1@eebd94deab4f9129a6d730c832167b6dce216fa6/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EverJun2/MLweek7_1/resolve/eebd94deab4f9129a6d730c832167b6dce216fa6/training_args.bin
5.84 kB
- Xet hash:
- 448920fb5de1b1b3ea912b6774b1540c563e6512781cf88bffaf5150d9a4a5ce
- Size of remote file:
- 5.84 kB
- SHA256:
- 35900e1196c267bf4eb28ca2ddde84f21c937efd810afac91f7c3eb5aa66949b
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