add checkpoint

README.md

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 license: cc-by-nc-4.0
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+
+
+<div align="center">
+
+# Omnidata (Steerable Datasets)
+**A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV 2021)**
+
+  
+[`Project Website`](https://omnidata.vision) &centerdot; [`Paper`](https://arxiv.org/abs/2110.04994) &centerdot; [**`>> [Github] <<`**](https://github.com/EPFL-VILAB/omnidata#readme) &centerdot; [`Data`](https://github.com/EPFL-VILAB/omnidata/tree/main/omnidata_tools/dataset#readme) &centerdot; [`Pretrained Weights`](https://github.com/EPFL-VILAB/omnidata-tools/tree/main/omnidata_tools/torch#readme) &centerdot; [`Annotator`](https://github.com/EPFL-VILAB/omnidata-tools/tree/main/omnidata_annotator#readme) &centerdot; 
+
+</div>
+
+
+# DPT-Hybrid trained for surface normal estimation or depth estimation
+Vision Transformer (ViT) model trained using a DPT (Dense Prediction Transformer) decoder.
+
+
+## Intended uses & limitations
+You can use this model for monocular surface normal estimation or depth estimation. 
+* Normal: estimates surface normals, a unit vector representing the tangent plane of the surface at each pixel.
+* Depth: estimates normalized depth, a relative depth rather then metric depth.
+
+
+## Models
+Models to estimate surface depth from RGB images.
+* Architecture: [DPT](https://github.com/isl-org/DPT)
+* Training resolutions: 384x384
+* Training data: [Omnidate dataset](https://github.com/EPFL-VILAB/omnidata/tree/main)
+* Input:
+  * Dimensions: 384x384
+  * Normalization: (normals: [0, 1], depth: [-1,1])
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{eftekhar2021omnidata,
+  title={Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets From 3D Scans},
+  author={Eftekhar, Ainaz and Sax, Alexander and Malik, Jitendra and Zamir, Amir},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={10786--10796},
+  year={2021}
+}
+```
+
+In case you use our latest pretrained models please also cite the following paper for 3D data augmentations:
+
+```bibtex
+@inproceedings{kar20223d,
+  title={3D Common Corruptions and Data Augmentation},
+  author={Kar, O{\u{g}}uzhan Fatih and Yeo, Teresa and Atanov, Andrei and Zamir, Amir},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={18963--18974},
+  year={2022}
+}
+```
+<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->
+> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)? 

omnidata_normal_dpt_hybrid.pth

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