Instructions to use delima87/Defect_vs_NoDefect_SewerInspection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use delima87/Defect_vs_NoDefect_SewerInspection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="delima87/Defect_vs_NoDefect_SewerInspection")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("delima87/Defect_vs_NoDefect_SewerInspection") model = AutoModel.from_pretrained("delima87/Defect_vs_NoDefect_SewerInspection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49f2176ed7484247e7eccd107a157f779274f75e98047d9fc30c14f704acab7e
- Size of remote file:
- 346 MB
- SHA256:
- 85cb2ebcf74eaf20bf9ae169efb57e2253cdbb280d76eea86d494176d8c5bf18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.