Instructions to use vaibhavprajapati22/Image_Denoising_RIDNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhavprajapati22/Image_Denoising_RIDNet with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vaibhavprajapati22/Image_Denoising_RIDNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cd9deefe0f8f5c6184edd6a397d3a61a2595ad55257a0ade224f15360415822
- Size of remote file:
- 24 MB
- SHA256:
- bc1b5cb5a6d6435f69e2b3f5a00030d90be601eb59dc1855e127200e86cb38a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.