Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: depth-estimation
+library_name: coreml
+tags:
+- depth
+- relative depth
+base_model:
+- depth-anything/Depth-Anything-V2-Small
+---
+
+# Depth Anything V2 Small (mlpackage)
+
+In this repo you can find:
+* The notebook which was used to convert [depth-anything/Depth-Anything-V2-Small](https://huggingface.co/depth-anything/Depth-Anything-V2-Small) into a CoreML package.
+* Both mlpackage files which can be opened in Xcode and used for Preview and development of macOS and iOS Apps
+* Performence and compute unit mapping report for these models as meassured on an iPhone 16 Pro Max
+* One model uses internal resolution of 518x518 ("Box") and the other 518x392 ("Landscape").
+* The "Landscape" is much faster than "Box" but will also give more "juggy" edges, due to the patch I applied to avoid bicubing upsampling (.diff file is also present in this repo)
+
+As a derivative work of Depth-Anything-V2-Small this port is also under apache-2.0
+
+![Xcode Preview](https://huggingface.co/LloydAI/DepthAnything_v2-Small-CoreML/resolve/main/sample_images/Xcode_Preview_DepthAnything_v2_Small_518x392_Landscape.jpg)
+
+
+## Citation of original work
+
+If you find this project useful, please consider citing:
+
+```bibtex
+@article{depth_anything_v2,
+  title={Depth Anything V2},
+  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
+  journal={arXiv:2406.09414},
+  year={2024}
+}
+
+@inproceedings{depth_anything_v1,
+  title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
+  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
+  booktitle={CVPR},
+  year={2024}
+}
+