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  3. README.md +1 -216
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  5. data/semisupervised_set/depth_sfm_manual202204/part0/atanasie_DJI_0652_full_4501.npz +0 -3
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.gitignore DELETED
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- raw_data/representations/
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- *.mp4
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- *.MP4
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- voting_learn_data/
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- *.npz
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- list.txt
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- *.tar.gz
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- copyfiles3.txt
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- raw_data/npz_540p
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- exception.txt
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- __pycache__
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- error.txt
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- sanity_check.py
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- commands.txt
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- raw_data/npz_540p_2/
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- here.csv
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- *.ttf
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- scripts/results/
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- scripts/collage*
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README.md CHANGED
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  ---
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- tags:
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- - videos
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- - video
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- - uav
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- - drones
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- - multitask
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- - multimodal
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  ---
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- # Dronescapes dataset
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-
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- 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)
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-
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- ![Logo](logo.png)
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-
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- Note: We are extending this dataset in another repository. GT data for benchmarking is the same, but we are generating
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- modalities as inputs: [dronescapes-2024](https://huggingface.co/datasets/Meehai/dronescapes-2024).
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-
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- # 1. Downloading the data
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-
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- ```
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- git lfs install # Make sure you have git-lfs installed (https://git-lfs.com)
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- git clone https://huggingface.co/datasets/Meehai/dronescapes
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- ```
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-
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- Note: the dataset has about 200GB, so it may take a while to clone it.
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-
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- ## 1.2 Low level data for the dataset (GPS, camera rotation matrices)
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-
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- <details>
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- <summary> Click to expand </summary>
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-
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- ### 1.2.1 Convert Camera Normals to World Normals
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-
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- This is an optional step, but for some use cases, it may be better to use world normals instead of camera normals, which
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- are provided by default in `normals_sfm_manual202204`. To convert, we provide camera rotation matrices in
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- `raw_data/camera_matrics.tar.gz` for all 8 scenes that also have SfM.
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-
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- In order to convert, use this function (for each npz file):
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-
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- ```
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- def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) -> np.ndarray:
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- normals = (normals.copy() - 0.5) * 2 # [-1:1] -> [0:1]
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- camera_normals = camera_normals @ np.linalg.inv(rotation_matrix)
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- camera_normals = (camera_normals / 2) + 0.5 # [0:1] => [-1:1]
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- return np.clip(camera_normals, 0.0, 1.0)
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- ```
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-
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- ### 1.2.2 Raw camera location
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-
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- The GPS location (lat/long/height) is in the `raw_data/raw_camera_info.tar.gz`. Each file over there is an archive over the
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- original videos, so the train/val/test splits files from `raw_data/txt_files` must be used to map to proper indices. Each
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- archive contains a bunch of raw data, however the key `lat_long_height` should provide the GPS location.
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-
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- Note: there is no GPS for the norway scene, the log seems to have been lost.
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-
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- </details>
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-
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- ## 2. Using the data
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-
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- As per the split from the paper:
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-
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- <summary> Split </summary>
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- <img src="split.png" width="500px">
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-
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- The data is in the `data*` directory with 1 sub-directory for each split above (and a few more variants).
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-
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- The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
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- it, you should get a collage with all the default tasks, like this:
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-
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- ![Collage](collage.png)
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-
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- For a CLI-only method, you can use the provided reader as well:
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-
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- ```
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- python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories in data/
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- ```
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-
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- <details>
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- <summary> Expected output </summary>
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-
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- ```
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- [MultiTaskDataset]
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- - Path: '/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/test_set_annotated_only'
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- - 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)]
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- - Length: 116
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- - Handle missing data mode: 'fill_none'
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- == Shapes ==
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- {'depth_dpt': torch.Size([540, 960]),
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- 'depth_sfm_manual202204': torch.Size([540, 960]),
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- 'depth_ufo': torch.Size([540, 960, 1]),
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- 'edges_dexined': torch.Size([540, 960]),
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- 'edges_gb': torch.Size([540, 960, 1]),
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- 'normals_sfm_manual202204': torch.Size([540, 960, 3]),
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- 'opticalflow_rife': torch.Size([540, 960, 2]),
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- 'rgb': torch.Size([540, 960, 3]),
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- 'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960, 8]),
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- 'semantic_segprop8': torch.Size([540, 960, 8]),
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- 'softseg_gb': torch.Size([540, 960, 3])}
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- == Random loaded item ==
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- {'depth_dpt': tensor[540, 960] n=518400 (2.0Mb) x∈[0.043, 1.000] μ=0.341 σ=0.418,
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- 'depth_sfm_manual202204': None,
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- 'depth_ufo': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0.115, 0.588] μ=0.297 σ=0.138,
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- 'edges_dexined': tensor[540, 960] n=518400 (2.0Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
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- 'edges_gb': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0., 1.000] μ=0.063 σ=0.100,
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- 'normals_sfm_manual202204': None,
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- 'opticalflow_rife': tensor[540, 960, 2] n=1036800 (4.0Mb) x∈[-0.004, 0.005] μ=0.000 σ=0.000,
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- 'rgb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 1.000] μ=0.392 σ=0.238,
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- 'semantic_mask2former_swin_mapillary_converted': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'semantic_segprop8': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'softseg_gb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
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- == Random loaded batch ==
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- {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.043, 1.000] μ=0.340 σ=0.417,
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- 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) NaN!,
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- 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.115, 0.588] μ=0.296 σ=0.137,
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- 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
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- 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.063 σ=0.102,
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- 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) NaN!,
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- 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.004, 0.006] μ=0.000 σ=0.000,
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- 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.393 σ=0.238,
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- 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
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- == Random loaded batch using torch DataLoader ==
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- {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.025, 1.000] μ=0.216 σ=0.343,
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- 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.562 σ=0.335 NaN!,
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- 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.100, 0.580] μ=0.290 σ=0.128,
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- 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001,
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- 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.079 σ=0.116,
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- 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0.000, 1.000] μ=0.552 σ=0.253 NaN!,
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- 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.013, 0.016] μ=0.000 σ=0.004,
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- 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.338 σ=0.237,
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- 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331,
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- 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001}
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- ```
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- </details>
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-
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- ## 3. Evaluation for semantic segmentation
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-
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- We evaluate in the paper on the 3 test scenes (unsees at train) as well as the semi-supervised scenes (seen, but
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- different split) against the human annotated frames. The general evaluation script is in
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- `scripts/evaluate_semantic_segmentation.py`.
144
-
145
- General usage is:
146
- ```
147
- python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn
148
- [--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm]
149
- ```
150
-
151
- <details>
152
- <summary> Script explanation </summary>
153
- The script is a bit convoluted, so let's break it into parts:
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-
155
- - `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset
156
- - y_dir/1.npz, ..., y_dir/N.npz
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- - gt_dir/1.npz, ..., gt_dir.npz
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- - `classes` A list of classes in the order that they appear in the predictions and gt files
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- - `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as
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- the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers
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- below.
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- - `scenes` if the `y_dir` and `gt_dir` contains multiple scenes that you want to evaluate separately, the script allows
163
- you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are
164
- actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script
165
- outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level.
166
- </details>
167
-
168
- <details>
169
- <summary> Reproducing paper results for Mask2Former </summary>
170
-
171
- ```
172
- python scripts/evaluate_semantic_segmentation.py \
173
- data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # Mask2Former example, use yours here!
174
- data/test_set_annotated_only/semantic_segprop8/ \
175
- -o results.csv \
176
- --classes land forest residential road little-objects water sky hill \
177
- --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \
178
- --scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full
179
- ```
180
-
181
- Should output:
182
- ```
183
- scene iou f1
184
- barsana_DJI_0500_0501_combined_sliced_2700_14700 63.371 75.338
185
- comana_DJI_0881_full 60.559 73.779
186
- norway_210821_DJI_0015_full 37.986 45.939
187
- mean 53.972 65.019
188
-
189
- ```
190
-
191
- Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually):
192
-
193
- ```
194
- iou f1
195
- scene
196
- all 60.456 73.261
197
- ```
198
- </details>
199
-
200
- ### 3.1 Official benchmark
201
-
202
- #### IoU
203
-
204
- | method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full |
205
- |:-|:-|:-|:-|:-|:-|
206
- | [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 |
207
- | [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 |
208
- | [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf)[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 |
209
- | [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf)[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 |
210
- | [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 |
211
-
212
- [^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).
213
-
214
- #### F1 Score
215
-
216
- | method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full |
217
- |:-|:-|:-|:-|:-|:-|
218
- | [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 |
 
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