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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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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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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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# 1. Downloading the data |
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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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Note: the dataset has about 200GB, so it may take a while to clone it. |
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## 2. Using the data |
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As per the split from the paper: |
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<summary> Split </summary> |
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<img src="split.png" width="500px"> |
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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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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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For a CLI-only method, you can use the provided reader as well: |
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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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<details> |
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<summary> Expected output </summary> |
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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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## 3. Evaluation for semantic segmentation |
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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`. |
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General usage is: |
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``` |
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python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn |
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[--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm] |
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``` |
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<details> |
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<summary> Script explanation </summary> |
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The script is a bit convoluted, so let's break it into parts: |
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- `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset |
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- 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 |
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you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are |
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actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script |
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outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level. |
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</details> |
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<details> |
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<summary> Reproducing paper results for Mask2Former </summary> |
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``` |
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python scripts/evaluate_semantic_segmentation.py \ |
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data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # Mask2Former example, use yours here! |
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data/test_set_annotated_only/semantic_segprop8/ \ |
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-o results.csv \ |
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--classes land forest residential road little-objects water sky hill \ |
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--class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \ |
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--scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full |
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``` |
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Should output: |
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``` |
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scene iou f1 |
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barsana_DJI_0500_0501_combined_sliced_2700_14700 63.371 75.338 |
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comana_DJI_0881_full 60.559 73.779 |
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norway_210821_DJI_0015_full 37.986 45.939 |
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mean 53.972 65.019 |
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``` |
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Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually): |
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``` |
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iou f1 |
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scene |
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all 60.456 73.261 |
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``` |
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</details> |
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### 3.1 Official benchmark |
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#### IoU |
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| method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full | |
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|:-|:-|:-|:-|:-|:-| |
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| [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 | |
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| [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 | |
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| [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf)[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 | |
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| [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf)[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 | |
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| [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 | |
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[^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). |
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#### F1 Score |
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| method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full | |
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|:-|:-|:-|:-|:-|:-| |
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| [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 | |
