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Dronescapes dataset
As introduced in our ICCV 2023 workshop paper: link
1. Downloading the data
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.
Option 2. Generating the dataset from raw videos and basic labels .
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.
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 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)
2. Using the data
As per the split from the paper:
Split
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
The simplest way to explore the data is to use the provided notebook. 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/
Expected output
[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}
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]
Script explanation
The script is a bit convoluted, so let's break it into parts:y_dir
andgt_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 filesclass_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 they_dir
andgt_dir
contains multiple scenes that you want to evaluate separately, the script allows you to pass the prefix of all the scenes. For example, indata/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.
Reproducing paper results for Mask2Former
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
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 | 216M | 53.97 | 63.37 | 60.55 | 37.98 |
NGC(LR) | 32M | 40.75 | 46.51 | 45.59 | 30.17 |
CShift[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 |
NGC[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 |
SafeUAV[^1] | 1.1M | 32.79 | n/a | n/a | n/a |
[^1]: reported in the Dronescapes paper.
F1 Score
method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full |
---|---|---|---|---|---|
Mask2Former | 216M | 65.01 | 75.33 | 73.77 | 45.93 |
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