### Genral-purpose models
The general-purpose models are affine-invariant and as such need a pre-alignment step before an error can be computed.

Sample code for NYUv2 can be found here:
https://gist.github.com/ranftlr/a1c7a24ebb24ce0e2f2ace5bce917022

Sample code for KITTI can be found here:
https://gist.github.com/ranftlr/45f4c7ddeb1bbb88d606bc600cab6c8d


### KITTI
* Remove images from `/input/` and `/output_monodepth/` folders
* Download `kitti_eval_dataset.zip` https://drive.google.com/file/d/1GbfMGuwg2VS06Vl75-_tB5FDj9EOrjl0/view?usp=sharing and unzip it in the `/input/` folder (or follow this repository https://github.com/cogaplex-bts/bts to get RGB and Depth images from list [eigen_test_files_with_gt.txt](https://github.com/cogaplex-bts/bts/blob/master/train_test_inputs/eigen_test_files_with_gt.txt) )
* Download [dpt_hybrid_kitti-cb926ef4.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_kitti-cb926ef4.pt) model and place it in the `/weights/` folder
* Download [eval_with_pngs.py](https://raw.githubusercontent.com/cogaplex-bts/bts/5a55542ebbe849eb85b5ce9592365225b93d8b28/utils/eval_with_pngs.py) in the root folder
* `python run_monodepth.py --model_type dpt_hybrid_kitti --kitti_crop --absolute_depth`
* `python ./eval_with_pngs.py --pred_path ./output_monodepth/ --gt_path ./input/gt/ --dataset kitti --min_depth_eval 1e-3 --max_depth_eval 80 --garg_crop --do_kb_crop`

Result:
```
Evaluating 697 files
GT files reading done
45 GT files missing
Computing errors
     d1,      d2,      d3,  AbsRel,   SqRel,    RMSE, RMSElog,   SILog,   log10
  0.959,   0.995,   0.999,   0.062,   0.222,   2.575,   0.092,   8.282,   0.027
Done.
```

----

### NYUv2
* Remove images from `/input/` and `/output_monodepth/` folders
* Download `nyu_eval_dataset.zip` https://drive.google.com/file/d/1b37uu-bqTZcSwokGkHIOEXuuBdfo80HI/view?usp=sharing and unzip it in the `/input/` folder (or follow this repository https://github.com/cogaplex-bts/bts to get RGB and Depth images from list [nyudepthv2_test_files_with_gt.txt](https://github.com/cogaplex-bts/bts/blob/master/train_test_inputs/nyudepthv2_test_files_with_gt.txt) )
* Download [dpt_hybrid_nyu-2ce69ec7.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_nyu-2ce69ec7.pt) model (**or a new model** that is fine-tuned with slightly different hyperparameters [dpt_hybrid_nyu_new-217f207d.pt](https://drive.google.com/file/d/1Nxv2OiqhAMosBL2a3pflamTW39dMjaSp/view?usp=sharing)  ) and place it in the `/weights/` folder
* Download [eval_with_pngs.py](https://raw.githubusercontent.com/cogaplex-bts/bts/5a55542ebbe849eb85b5ce9592365225b93d8b28/utils/eval_with_pngs.py) in the root folder
* `python run_monodepth.py --model_type dpt_hybrid_nyu --absolute_depth`
(or **for new model** `python run_monodepth.py --model_type dpt_hybrid_nyu --absolute_depth --model_weights weights/dpt_hybrid_nyu_new-217f207d.pt` )
* `python ./eval_with_pngs.py --pred_path ./output_monodepth/ --gt_path ./input/gt/ --dataset nyu --max_depth_eval 10  --eigen_crop`

Result (old model) - **from paper**:
```
Evaluating 654 files
GT files reading done
0 GT files missing
Computing errors
     d1,      d2,      d3,  AbsRel,   SqRel,    RMSE, RMSElog,   SILog,   log10
  0.904,   0.988,   0.998,   0.109,   0.054,   0.357,   0.129,   9.521,   0.045
Done.
```

Result (new model):
```
GT files reading done
697 GT files missing
Computing errors
     d1,      d2,      d3,  AbsRel,   SqRel,    RMSE, RMSElog,   SILog,   log10
  0.905,   0.988,   0.998,   0.109,   0.055,   0.357,   0.129,   9.427,   0.045
Done.
```