Instructions to use shubhamWi91/train46 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train46 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train46")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train46") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train46", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train46: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/shubhamWi91/train46/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train46/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train46/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 0ed59bf75d5351380c82e47ef07a5a8129efe925671751ecc3a5a77e9e1c30c6
- Size of remote file:
- 243 MB
- SHA256:
- 3c0c7089949c57117ae57c75f272c41bd9f0245ae191a00147460cec5bd6c800
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.