Instructions to use shubhamWi91/train53 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train53 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train53")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train53") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train53", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shubhamWi91/train53: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/shubhamWi91/train53/resolve/main/training_args.bin
- Command line
-
hf download hf://shubhamWi91/train53/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shubhamWi91/train53/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- dba44b044395126fd113acaead743f761f81ed0b192844addc1a2205746a7fb7
- Size of remote file:
- 4.09 kB
- SHA256:
- c96bd1fcaa011f83313857bb42ade0226352949bd23922067cb644b0da2f64a3
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