README updates

README.md

@@ -1,339 +1,9 @@
-# Dronescapes dataset
+# Dronescapes 2024 dataset
 
-As introduced in our ICCV 2023 workshop paper: [link](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf)
+This dataset is an extension of the original [dronescapes dataset](https://huggingface.co/dataset/Meehai/dronescapes) with new modalities generated using VRE 100% from scratch. The only data that is not generable by VRE is the Ground Truth: semantic (human annotated), depth & normals (SfM).
 
 ![Logo](logo.png)
 
-# 1. Downloading the data
+WIP
 
-## Option 1. Download the pre-processed dataset from HuggingFace repository
-
-```
-git lfs install # Make sure you have git-lfs installed (https://git-lfs.com)
-git clone https://huggingface.co/datasets/Meehai/dronescapes
-```
-
-Note: the dataset has about 500GB, so it may take a while to clone it.
-
-<details>
-<summary> <b> Option 2. Generating the dataset from raw videos and basic labels </b>.</summary>
-
-Recommended if you intend on understanding how the dataset was created or add new videos or representations.
-
-### 1.2.1 Raw videos
-
-Follow the commands in each directory under `raw_data/videos/*/commands.txt` if you want to start from the 4K videos.
-
-If you only want the 540p videos as used in the paper, they are already provided in the `raw_data/videos/*` directories.
-
-### 1.2.2 Semantic segmentation labels (human annotated)
-
-These were human annotated and then propagated using [segprop](https://github.com/vlicaret/segprop).
-
-```bash
-cd raw_data/
-tar -xzvf segprop_npz_540.tar.gz
-```
-
-### 1.2.3 Generate the rest of the representations
-
-We use the [video-representations-extractor](https://gitlab.com/meehai/video-representations-extractor) to generate
-the rest of the labels using pre-traing networks or algoritms.
-
-Install it via `pip install video-representations-extractor` (or follow the README over there for docker or local env)
-
-```
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=0 vre raw_data/videos/atanasie_DJI_0652_full/atanasie_DJI_0652_full_540p.mp4 -o raw_data/npz_540p/atanasie_DJI_0652_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=1 vre raw_data/videos/barsana_DJI_0500_0501_combined_sliced_2700_14700/barsana_DJI_0500_0501_combined_sliced_2700_14700_540p.mp4 -o raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=2 vre raw_data/videos/comana_DJI_0881_full/comana_DJI_0881_full_540p.mp4 -o raw_data/npz_540p/comana_DJI_0881_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=3 vre raw_data/videos/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110_540p.mp4 -o raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=4 vre raw_data/videos/herculane_DJI_0021_full/herculane_DJI_0021_full_540p.mp4 -o raw_data/npz_540p/herculane_DJI_0021_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=5 vre raw_data/videos/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715_540p.mp4 -o raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=6 vre raw_data/videos/norway_210821_DJI_0015_full/norway_210821_DJI_0015_full_540p.mp4 -o raw_data/npz_540p/norway_210821_DJI_0015_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=7 vre raw_data/videos/olanesti_DJI_0416_full/olanesti_DJI_0416_full_540p.mp4 -o raw_data/npz_540p/olanesti_DJI_0416_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=0 vre raw_data/videos/petrova_DJI_0525_0526_combined_sliced_2850_11850/petrova_DJI_0525_0526_combined_sliced_2850_11850_540p.mp4 -o raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=1 vre raw_data/videos/slanic_DJI_0956_0957_combined_sliced_780_9780/slanic_DJI_0956_0957_combined_sliced_780_9780_540p.mp4 -o raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite 
-```
-
-Note: `depth_sfm`, `normals_sfm` and `depth_ufo` are not available in VRE. Contact us for more info about them.
-
-Note: Add `--representations "rgb" "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" "softseg_gb"` to control if you only want a subset of the representations.
-
-Note: Some batch sizes are overwritten in the config itself.
-
-### 1.2.4 Convert Mask2Former from Mapillary classes to segprop8 classes
-
-Since we are using pre-trained Mask2Former which has either mapillary or COCO panoptic classes, we need to convert them to dronescapes-compatible (8) classes.
-
-To do this, we use the `scripts/convert_m2f_to_dronescapes.py` script:
-```
-python scripts/convert_m2f_to_dronescapes.py in_dir out_dir mapillary/coco [--overwrite]
-```
-
-```
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary_converted mapillary
-python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary_converted mapillary
-```
-
-### 1.2.5 Check counts for consistency
-
-Run: `bash scripts/count_npz.sh raw_data/npz_540p`. At this point it should return:
-| scene     |   rgb |   depth_dpt |   depth_sfm_manual20.. |   edges_dexined |   normals_sfm_manual.. |   opticalflow_rife |   semantic_mask2form.. |   semantic_segprop8 |
-|:----------|------:|------------:|-----------------------:|----------------:|-----------------------:|-------------------:|-----------------------:|--------------------:|
-| atanasie  |  9021 |        9021 |                   9020 |            9021 |                   9020 |               9021 |                   9021 |                9001 |
-| barsana   | 12001 |       12001 |                  12001 |           12001 |                  12001 |              12000 |                  12001 |                1573 |
-| comana    |  9022 |        9022 |                      0 |            9022 |                      0 |               9022 |                   9022 |                1210 |
-| gradistei |  9601 |        9601 |                   9600 |            9601 |                   9600 |               9600 |                   9601 |                1210 |
-| herculane |  9022 |        9022 |                   9021 |            9022 |                   9021 |               9022 |                   9022 |                1210 |
-| jupiter   | 11066 |       11066 |                  11065 |           11066 |                  11065 |              11066 |                  11066 |                1452 |
-| norway    |  2983 |        2983 |                      0 |            2983 |                      0 |               2983 |                   2983 |                2941 |
-| olanesti  |  9022 |        9022 |                   9021 |            9022 |                   9021 |               9022 |                   9022 |                1210 |
-| petrova   |  9001 |        9001 |                   9001 |            9001 |                   9001 |               9000 |                   9001 |                1210 |
-| slanic    |  9001 |        9001 |                   9001 |            9001 |                   9001 |               9000 |                   9001 |                9001 |
-
-### 1.2.6. Split intro train, validation, semisupervised and train
-
-We include 8 splits: 4 using only GT annotated semantic data and 4 using all available data (i.e. segproped between
-annotated data). The indexes are taken from `txt_files/*`, i.e. `txt_files/manually_adnotated_files/test_files_116.txt`
-refers to the fact that the (unseen at train time) test set (norway + petrova + barsana) contains 116 manually
-annotated semantic files. We include all representations from above, not just semantic for all possible splits.
-Adding new representations is as simple as running VRE on the 540p mp4 file
-
-```
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/train_files_11664.txt -o data/train_set --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/val_files_605.txt -o data/validation_set --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/semisup_files_11299.txt -o data/semisupervised_set --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/test_files_5603.txt -o data/test_set --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/train_files_218.txt -o data/train_set_annotated_only --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/val_files_15.txt -o data/validation_set_annotated_only --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/semisup_files_207.txt -o data/semisupervised_set_annotated_nly --overwrite
-python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/test_files_116.txt -o data/test_set_annotated_only --overwrite
-```
-
-Note: `add --copy_files` if you want to make copies instead of using symlinks.
-
-Upon calling this, you should be able to see something like this:
-```
-user> ls data/*
-data/semisupervised_set:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/semisupervised_set_annotated_nly:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/test_set:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/test_set_annotated_nly:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/train_set:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/train_set_annotated_only:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/validation_set:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-
-data/validation_set_annotated_only:
-depth_dpt               edges_dexined             opticalflow_rife  semantic_mask2former_swin_mapillary_converted
-depth_sfm_manual202204  normals_sfm_manual202204  rgb               semantic_segprop8
-```
-
-### 1.2.7 Convert Camera Normals to World Normals
-
-This is an optional step, but for some use cases, it may be better to use world normals instead of camera normals, which
-are provided by default in `normals_sfm_manual202204`. To convert, we provide camera rotation matrices in
-`raw_data/camera_matrics.tar.gz` for all 8 scenes that also have SfM.
-
-In order to convert, use this function (for each npz file):
-
-```
-def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) -> np.ndarray:
-  normals = (normals.copy() - 0.5) * 2 # [-1:1] -> [0:1]
-  camera_normals = camera_normals @ np.linalg.inv(rotation_matrix)
-  camera_normals = (camera_normals / 2) + 0.5 # [0:1] => [-1:1]
-  return np.clip(camera_normals, 0.0, 1.0)
-```
-
-</details>
-
-## 2. Using the data
-
-As per the split from the paper:
-
-<details>
-<summary> Split </summary>
-<img src="split.png">
-</details>
-
-The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface).
-
-## 2.1 Using the provided viewer
-
-![Collage](collage.png)
-
-The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
-it, you should get a collage with all the default tasks, like the picture at the top.
-
-For a CLI-only method, you can use the provided reader as well:
-
-```
-python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories in data/
-```
-
-<details>
-<summary> Expected output </summary>
-
-```
-[MultiTaskDataset]
- - Path: '/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/test_set_annotated_only'
- - Tasks (11): [DepthRepresentation(depth_dpt), DepthRepresentation(depth_sfm_manual202204), DepthRepresentation(depth_ufo), ColorRepresentation(edges_dexined), EdgesRepresentation(edges_gb), NpzRepresentation(normals_sfm_manual202204), OpticalFlowRepresentation(opticalflow_rife), ColorRepresentation(rgb), SemanticRepresentation(semantic_mask2former_swin_mapillary_converted), SemanticRepresentation(semantic_segprop8), ColorRepresentation(softseg_gb)]
- - Length: 116
- - Handle missing data mode: 'fill_none'
-== Shapes ==
-{'depth_dpt': torch.Size([540, 960]),
- 'depth_sfm_manual202204': torch.Size([540, 960]),
- 'depth_ufo': torch.Size([540, 960, 1]),
- 'edges_dexined': torch.Size([540, 960]),
- 'edges_gb': torch.Size([540, 960, 1]),
- 'normals_sfm_manual202204': torch.Size([540, 960, 3]),
- 'opticalflow_rife': torch.Size([540, 960, 2]),
- 'rgb': torch.Size([540, 960, 3]),
- 'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960, 8]),
- 'semantic_segprop8': torch.Size([540, 960, 8]),
- 'softseg_gb': torch.Size([540, 960, 3])}
-== Random loaded item ==
-{'depth_dpt': tensor[540, 960] n=518400 (2.0Mb) x∈[0.043, 1.000] μ=0.341 σ=0.418,
- 'depth_sfm_manual202204': None,
- 'depth_ufo': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0.115, 0.588] μ=0.297 σ=0.138,
- 'edges_dexined': tensor[540, 960] n=518400 (2.0Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
- 'edges_gb': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0., 1.000] μ=0.063 σ=0.100,
- 'normals_sfm_manual202204': None,
- 'opticalflow_rife': tensor[540, 960, 2] n=1036800 (4.0Mb) x∈[-0.004, 0.005] μ=0.000 σ=0.000,
- 'rgb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 1.000] μ=0.392 σ=0.238,
- 'semantic_mask2former_swin_mapillary_converted': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'semantic_segprop8': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'softseg_gb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
-== Random loaded batch ==
-{'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.043, 1.000] μ=0.340 σ=0.417,
- 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) NaN!,
- 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.115, 0.588] μ=0.296 σ=0.137,
- 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
- 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.063 σ=0.102,
- 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) NaN!,
- 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.004, 0.006] μ=0.000 σ=0.000,
- 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.393 σ=0.238,
- 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
-== Random loaded batch using torch DataLoader ==
-{'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.025, 1.000] μ=0.216 σ=0.343,
- 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.562 σ=0.335 NaN!,
- 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.100, 0.580] μ=0.290 σ=0.128,
- 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
- 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.079 σ=0.116,
- 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0.000, 1.000] μ=0.552 σ=0.253 NaN!,
- 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.013, 0.016] μ=0.000 σ=0.004,
- 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.338 σ=0.237,
- 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
- 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
-```
-</details>
-
-## 3. Evaluation for semantic segmentation
-
-We evaluate in the paper on the 3 test scenes (unsees at train) as well as the semi-supervised scenes (seen, but
-different split) against the human annotated frames. The general evaluation script is in
-`scripts/evaluate_semantic_segmentation.py`.
-
-General usage is:
-```
-python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn
-[--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm]
-```
-
-<details>
-<summary> Script explanation </summary>
-The script is a bit convoluted, so let's break it into parts:
-
-- `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset (y_dir/1.npz, gt_dir/55.npz etc.)
-- `classes` A list of classes in the order that they appear in the predictions and gt files
-- `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as
-the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers
-below.
-- `scenes` if the `y_dir` and `gt_dir` contains multiple scenes that you want to evaluate separately, the script allows
-you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are
-actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script
-outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level.
-</details>
-
-<details>
-<summary> Reproducing paper results for Mask2Former </summary>
-
-```
-python scripts/evaluate_semantic_segmentation.py \
-  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \
-  data/test_set_annotated_only/semantic_segprop8/ \
-  -o results.csv \
-  --classes land forest residential road little-objects water sky hill \
-  --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \
-  --scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full
-```
-
-Should output:
-```
-scene                                             iou      f1                                                           
-barsana_DJI_0500_0501_combined_sliced_2700_14700  63.371  75.338
-comana_DJI_0881_full                              60.559  73.779
-norway_210821_DJI_0015_full                       37.986  45.939
-mean                                              53.972  65.019
-
-```
-
-Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually):
-
-```
-          iou      f1
-scene
-all    60.456  73.261
-```
-</details>
-
-### 3.1 Official benchmark
-
-#### IoU
-
-| method | #paramters |  average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full |  norway_210821_DJI_0015_full |
-|:-|:-|:-|:-|:-|:-|
-| [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 53.97 | 63.37 | 60.55 | 37.98 |
-| [NGC(LR)](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 32M | 40.75  | 46.51 | 45.59 | 30.17 |
-| [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf)[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 |
-| [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf)[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 |
-| [SafeUAV](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11130/Marcu_SafeUAV_Learning_to_estimate_depth_and_safe_landing_areas_for_ECCVW_2018_paper.pdf)[^1] | 1.1M | 32.79 | n/a | n/a | n/a |
-
-[^1]: reported in the [Dronescapes paper](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf).
-
-#### F1 Score
-
-| method | #paramters |  average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full |  norway_210821_DJI_0015_full |
-|:-|:-|:-|:-|:-|:-|
-| [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 65.01 | 75.33 | 73.77 | 45.93 |