added licenses and readme

LICENSE

@@ -0,0 +1,43 @@
+MIT License
+
+Copyright (c) 2023 ShuweiShao
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+MIT License
+
+Copyright (c) 2024 Umut YILDIRIM <hope@umutyildirim.com>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -10,4 +10,149 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<div align="center">
+
+<h1>IEBins: Iterative Elastic Bins for Monocular Depth Estimation</h1>
+
+<div>
+    <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=ecZHSVQAAAAJ' target='_blank'>Shuwei Shao</a><sup>1</sup>&emsp;
+    <a target='_blank'>Zhongcai Pei</a><sup>1</sup>&emsp;
+    <a target='_blank'>Xingming Wu</a><sup>1</sup>&emsp;
+    <a target='_blank'>Zhong Liu</a><sup>1</sup>&emsp;
+    <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=5PoZrcYAAAAJ' target='_blank'>Weihai Chen</a><sup>2</sup>&emsp;
+    <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=LiUX7WQAAAAJ' target='_blank'>Zhengguo Li</a><sup>3</sup>
+</div>
+<div>
+    <sup>1</sup>Beihang University, <sup>2</sup>Anhui University, <sup>3</sup>A*STAR
+</div>
+
+<div>
+    <h4 align="center">
+        • <a href="https://arxiv.org/abs/2309.14137" target='_blank'>NeurIPS 2023</a> •
+    </h4>
+</div>
+
+[![KITTI Benchmark](https://img.shields.io/badge/KITTI%20Benchmark-2nd%20among%20all%20at%20submission%20time-blue)](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction)
+[![Hugging Space Badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](umuthopeyildirim/IEBins-Depth-Perception)
+
+## Abstract
+
+<div style="text-align:center">
+<img src="assets/teaser.jpg"  width="80%" height="80%">
+</div>
+
+</div>
+<strong>We propose a novel concept of iterative elastic bins for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. </strong>
+
+---
+
+</div>
+
+## Installation
+
+```
+conda create -n iebins python=3.8
+conda activate iebins
+conda install pytorch=1.10.0 torchvision cudatoolkit=11.1
+pip install matplotlib, tqdm, tensorboardX, timm, mmcv, open3d
+```
+
+## Datasets
+
+You can prepare the datasets KITTI and NYUv2 according to [here](https://github.com/cleinc/bts/tree/master/pytorch) and download the SUN RGB-D dataset from [here](https://rgbd.cs.princeton.edu/), and then modify the data path in the config files to your dataset locations.
+
+## Training
+
+First download the pretrained encoder backbone from [here](https://github.com/microsoft/Swin-Transformer), and then modify the pretrain path in the config files. If you want to train the KITTI_Official model, first download the pretrained encoder backbone from [here](https://drive.google.com/file/d/1qjDnMwmEz0k0XWh7GP2aNPGiAjvOPF_5/view?usp=drive_link), which is provided by [MIM](https://github.com/SwinTransformer/MIM-Depth-Estimation).
+
+Training the NYUv2 model:
+
+```
+python iebins/train.py configs/arguments_train_nyu.txt
+```
+
+Training the KITTI_Eigen model:
+
+```
+python iebins/train.py configs/arguments_train_kittieigen.txt
+```
+
+Training the KITTI_Official model:
+
+```
+python iebins_kittiofficial/train.py configs/arguments_train_kittiofficial.txt
+```
+
+## Evaluation
+
+Evaluate the NYUv2 model:
+
+```
+python iebins/eval.py configs/arguments_eval_nyu.txt
+```
+
+Evaluate the NYUv2 model on the SUN RGB-D dataset:
+
+```
+python iebins/eval_sun.py configs/arguments_eval_sun.txt
+```
+
+Evaluate the KITTI_Eigen model:
+
+```
+python iebins/eval.py configs/arguments_eval_kittieigen.txt
+```
+
+To generate KITTI Online evaluation data for the KITTI_Official model:
+
+```
+python iebins_kittiofficial/test.py --data_path path to dataset --filenames_file ./data_splits/kitti_official_test.txt --max_depth 80 --checkpoint_path path to pretrained checkpoint  --dataset kitti --do_kb_crop
+```
+
+## Qualitative Depth and Point Cloud Results
+
+You can download the qualitative depth results of [IEBins](https://arxiv.org/abs/2309.14137), [NDDepth](https://arxiv.org/abs/2309.10592), [NeWCRFs](https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html), [PixelFormer](https://openaccess.thecvf.com/content/WACV2023/html/Agarwal_Attention_Attention_Everywhere_Monocular_Depth_Prediction_With_Skip_Attention_WACV_2023_paper.html), [AdaBins](https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html) and [BTS](https://arxiv.org/abs/1907.10326) on the test sets of NYUv2 and KITTI_Eigen from [here](https://pan.baidu.com/s/1zaFe40mwpQ5cvdDlLZRrCQ?pwd=vfxd) and download the qualitative point cloud results of IEBins, NDDepth, NeWCRFS, PixelFormer, AdaBins and BTS on the NYUv2 test set from [here](https://pan.baidu.com/s/1WwpFuPBGBUaSGPEdThJ6Rw?pwd=n9rw).
+
+If you want to derive these results by yourself, please refer to the test.py.
+
+If you want to perform inference on a single image, run:
+
+```
+python iebins/inference_single_image.py --dataset kitti or nyu --image_path path to image --checkpoint_path path to pretrained checkpoint --max_depth 80 or 10
+```
+
+Then you can acquire the qualitative depth result.
+
+## Models
+
+| Model                | Abs Rel | Sq Rel | RMSE  |  a1   |  a2   |  a3   |                                                                                 Link                                                                                  |
+| -------------------- | :-----: | :----: | :---: | :---: | :---: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| NYUv2 (Swin-L)       |  0.087  | 0.040  | 0.314 | 0.936 | 0.992 | 0.998 | [[Google]](https://drive.google.com/file/d/14Rn-vxvpXO2EXRaWqCPmh2JufvOurwtl/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1E2KAHtQ-ul99RGp_G7QK1w?pwd=7o4d) |
+| NYUv2 (Swin-T)       |  0.108  | 0.061  | 0.375 | 0.893 | 0.984 | 0.996 | [[Google]](https://drive.google.com/file/d/1eYkTb3grbDitQ9tJdg1DhAOaGmqgHWHK/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1v5_MJtP0YOSoark9Yw1RaQ?pwd=2k5d) |
+| KITTI_Eigen (Swin-L) |  0.050  | 0.142  | 2.011 | 0.978 | 0.998 | 0.999 | [[Google]](https://drive.google.com/file/d/1xaVLDq7zJ-C2GtFvABolSUtK7gzvNQNd/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/16mRrKrr9PdZhuZ3ZlkmNlA?pwd=lcjd) |
+| KITTI_Eigen (Swin-T) |  0.056  | 0.169  | 2.205 | 0.970 | 0.996 | 0.999 | [[Google]](https://drive.google.com/file/d/1s0LXZmS6_Q4_H_0hmbOldPcVhlRw8Dut/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1xgeqIX5WP5F2MFwypMWV5A?pwd=ygfi) |
+
+| Model                     | SILog | Abs Rel | Sq Rel | RMSE |  a1   |  a2   |  a3   |                                               Link                                                |
+| ------------------------- | :---: | :-----: | :----: | :--: | :---: | :---: | :---: | :-----------------------------------------------------------------------------------------------: |
+| KITTI_Official (Swinv2-L) | 7.48  |  5.20   |  0.79  | 2.34 | 0.974 | 0.996 | 0.999 | [[Google]](https://drive.google.com/file/d/19ARBiDTIvtZSWJVvhbEWBcZMonXsiOX1/view?usp=drive_link) |
+
+## Citation
+
+If you find our work useful in your research please consider citing our paper:
+
+```
+@inproceedings{shao2023IEBins,
+title={IEBins: Iterative Elastic Bins for Monocular Depth Estimation},
+author={Shao, Shuwei and Pei, Zhongcai and Wu, Xingming and Liu, Zhong and Chen, Weihai and Li, Zhengguo},
+booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+year={2023}
+}
+```
+
+## Contact
+
+If you have any questions, please feel free to contact swshao@buaa.edu.cn.
+
+## Acknowledgement
+
+Our code is based on the implementation of [NeWCRFs](https://github.com/aliyun/NeWCRFs) and [BTS](https://github.com/cleinc/bts). We thank their excellent works.