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 pytorch_model.bin from shubhamWi91/train53: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/shubhamWi91/train53/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train53/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train53/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 6f2a193366f0c662b3fc288a55012d93b97a3739cf2653ddc1b9f8864b4ccee2
- Size of remote file:
- 243 MB
- SHA256:
- 9a3584bc93692afefee2b3be1f518acc6e11059aa23ff0e02b8fdd1d9e0359d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.