Instructions to use shubhamWi91/train84 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train84 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train84")# Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train84", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e7db306dd3d3454abf309b01c991b3a1145be350aad6907b44549d76f62adc0
- Size of remote file:
- 879 MB
- SHA256:
- 914d6e47ce5d0b741ecca508c5e0389b58f2ea9e12a11067d551c440f93dde93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.