Instructions to use sg485/Resnet50_Table_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sg485/Resnet50_Table_Transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="sg485/Resnet50_Table_Transformer")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("sg485/Resnet50_Table_Transformer") model = AutoModelForObjectDetection.from_pretrained("sg485/Resnet50_Table_Transformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9388ad36cdac6ff8bd9052e799c7d54b0af345e1c5976084af9e5dac33799e69
- Size of remote file:
- 167 MB
- SHA256:
- 131944041f491f7e5d01e64011ae8b97fdc8d3e0766571efc4e5e817b46198a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.