Update README.md

README.md

@@ -44,54 +44,4 @@ For KITTI, we generate the paired correspondence using a novel method that we de
 - [ARD-16] - The ARD-16 dataset consists of 4 numpy arrays (0..3). We use three (0..2) numpy arrays for training our model and the fourth numpy array for testing our model. The data is available [here](https://github.com/dslrproject/dslr/tree/master/Data).
 ``` bash
 
-## Pretrained Models
 
-Please download the pretrained weights from [this link](https://drive.google.com/drive/folders/1BVWLrdHw0bfsuzzL62S7xpWmmqgvysxd?usp=sharing) and save `.ckpt` weights inside `<repo root>/depth/checkpoints` directory.
-
-Also download the v1-5 checkpoint of [stable-diffusion](https://github.com/runwayml/stable-diffusion) and put it in the `<repo root>/checkpoints` directory. Please create an empty directory if you find that such a path does not exist. Note that this checkpoints folder is different from the one above. 
-
-## Inference
-
-To perform inference on any RGB image or video use the `infer_{outdoor,indoor}.sh` file. Set the `--img_path` argument to the image you would to get the depth for and the `--video_path` to the video from which to produce the depth. In case you only wish to infer on an img or video, simply remove the other argument. Then enter the `depth` directory by executing `cd depth` and run:
-
-1. **Infer on outdoor scenes**:
-`bash infer_outdoor.sh`
-
-2. **Infer on outdoor scenes**:
-`bash infer_indoor.sh`
-
-## Evaluation
-To evaluate the model performance on NYUv2 and KITTI datasets, use the `test_{kitti, nyu}` file. The trained models are publicly available, download the models using [above links](#pretrained-models). Then, navigate to the `depth` directory and follow the instructions outlined below:
-
-1. **Evaluate on NYUv2 dataset**: 
-`bash test_nyu.sh <path_to_saved_model_of_NYU>` 
-
-2. **Evaluate on KITTI dataset**: 
-`bash test_kitti.sh <path_to_saved_model_of_KITTI>`
-
-## Training 
-We trained our models on 32 batch size using 8xNVIDIA A100 GPUs. Inside the `train_{kitti,nyu}.sh` set the `NPROC_PER_NODE` variable and `--batch_size` argument to the desired values as per your system resources. For our method we set them as `NPROC_PER_NODE=8` and `--batch_size=4` (resulting in a total batch size of 32). Afterwards, navigate to the `depth` directory by executing `cd depth` and follow the instructions:
-
-1. **Train on NYUv2 dataset**: 
-`bash train_nyu.sh` 
-
-1. **Train on KITTI dataset**: 
-`bash train_kitti.sh`
-
-### Contact
-If you have any questions about our code or paper, kindly raise an issue on this repository.
-
-### Acknowledgment
-We thank [Kartik Anand](https://github.com/k-styles) for assistance with the experiments. 
-Our source code is inspired from [VPD](https://github.com/wl-zhao/VPD) and [PixelFormer](https://github.com/ashutosh1807/PixelFormer). We thank their authors for publicly releasing the code.
-
-### BibTeX (Citation)
-If you find our work useful in your research, please consider citing using:
-``` bibtex
-@article{patni2024ecodepth,
- title={ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation},
- author={Patni, Suraj and Agarwal, Aradhye and Arora, Chetan},
- journal={arXiv preprint arXiv:2403.18807},
- year={2024}
-}
-```