❗❗[09/10/2024] We corrected some numbers reported in Tables 1,3,4,5 and 10. Please refer to the last revision of the ECCV'24 paper.
🔥[24/09/2024]🚀 check out our ECCV'24 paper summarizing the BRAVO challenge 2024.
The 2024 BRAVO Challenge aims to benchmark semantic segmentation models on urban scenes undergoing diverse forms of natural degradation and realistic-looking synthetic corruption.
We promote the 2024 BRAVO Challenge in conjunction with the 3rd Workshop on Uncertainty Quantification for Computer Vision @ ECCV 2024.
We propose two tracks:
In this track, you must train your models exclusively on the Cityscapes dataset. This track evaluates the robustness of models trained with limited supervision and geographical diversity when facing unexpected corruptions observed in real-world scenarios.
In this track, you must train your models over a mix of datasets, whose choice is strictly limited to the list provided below, comprising both natural and synthetic domains. This track assesses the impact of fewer constraints on the training data on robustness.
Allowed training datasets for Track 2:
- Cityscapes
- BDD100k
- Mapillary Vistas
- India Driving Dataset
- WildDash 2
- GTA5 Dataset (synthetic)
- SHIFT Dataset (synthetic)
- The task is semantic segmentation with pixel-wise evaluation performed on the 19 semantic classes of Cityscapes.
- Models in each track must be trained using only the datasets allowed for that track.
- Employing generative models for data augmentation is strictly forbidden.
- All results must be reproducible. Participants must submit a white paper containing comprehensive technical details alongside their results (see details below). Participants must make models and inference code accessible.
- Evaluation will consider the 19 classes of Cityscapes (see below).
- Teams must register a single account for submitting to the evaluation server. An organization (e.g. a University) may have several teams with independent accounts only if the teams are not cooperating on the challenge.
We created the benchmark dataset with real, captured images and realistic-looking synthetic augmentations, repurposing existing datasets and combining them with newly generated data. The benchmark dataset comprises images from ACDC, SegmentMeIfYouCan, Out-of-context Cityscapes, and new synthetic data.
Get the full benchmark dataset at the following link: full BRAVO Dataset download link.
The dataset includes the following subsets (with individual download links):
bravo-ACDC: real scenes captured in adverse weather conditions, i.e., fog, night, rain, and snow. (download link or directly from ACDC website)
bravo-SMIYC: real scenes featuring out-of-distribution (OOD) objects rarely encountered on the road. (download link or directly from SMIYC website)
bravo-synrain: augmented scenes with synthesized raindrops on the camera lens. We augmented the validation images of Cityscapes and generated 500 images with raindrops. (download link)
bravo-synobjs: augmented scenes with inpainted synthetic OOD objects. We augmented the validation images of Cityscapes and generated 656 images with 26 OOD objects. (download link)
bravo-synflare: augmented scenes with synthesized light flares. We augmented the validation images of Cityscapes and generated 308 images with random light flares. (download link)
bravo-outofcontext: augmented scenes with random backgrounds. We augmented the validation images of Cityscapes and generated 329 images with random random backgrounds. (download link)
The evaluation is performed on the 19 semantic classes of Cityscapes.
The metrics are computed separately for 9 subsets of the benchmark dataset: ACDCfog, ACDCrain, ACDCnight, ACDCsnow, SMIYC, synrain, synobjs, synflare, and outofcontext.
The metrics are computed pixel-wise and averaged over the pixels of the images belonging to a subset.
For each subset, the following metrics are computed:
- mIoU: mean Intersection Over Union, the rate of corrected labeled pixels over all pixels.
- ECE: Expected Calibration Error, quantifying the mismatch between predicted confidence and actual accuracy.
- AUROC: Area Under the ROC Curve, over the binary criterion of a pixel being accurate, ranked by the predicted confidence level for the pixel.
- FPR@95: False Positive Rate when True Positive Rate is 95% computed in the ROC curve above.
- AUPR-Success: Area Under the Precision-Recall curve, over the same data as the AUROC.
- AUPR-Error: Area Under the Precision-Recall, on the reversed data (pixel being inaccurate, ranked by 1-confidence).
Those metrics are computed on all subsets, except for SMIYC, for all valid pixels of each image. Valid pixels are those not "invalidated" by extreme uncertainty, e.g., pixels behind the brightest areas of a flare, or behind an OOD object are considered invalid.
Those are the AUROC, AUPRC-Success, and FPR@95 metrics above, but over different data: the (ground-truth) status of a pixel being invalid ranked by reversed predicted confidence (1-confidence). Those metrics quantify whether the model attributes, as expected, less confidence to the invalid pixels.
Those metrics are computed for the subsets: SMIYC and synobjects.
We choose the harmonic mean as the aggregation statistic. We compute it for each subset, for all semantic metrics (semantic_hmean), and for all ood metrics (ood_hmean). In the aggregation, the ECE and the FPR@95 metrics enter reversed as 1.0-<original value>. The other metrics enter unmodified.
The official ranking metric for the public leaderboard is the bravo_index, which is the harmonic mean of the semantic_hmean and the ood_hmean.
For each input image "source.*", we require two submitted files "source_pred.png" for the semantic prediction and "source_conf.png" for the confidences.
For the class prediction files (_pred.png): PNG format, 8-bits, grayscale, with each pixel with a value from 0 to 19 corresponding to the 19 classes of Cityscapes, which are, in order:
| 0. road | 5. pole | 10. sky | 15. bus |
| 1. sidewalk | 6. traffic light | 11. person | 16. train |
| 2. building | 7. traffic sign | 12. rider | 17. motorcycle |
| 3. wall | 8. vegetation | 13. car | 18. bicycle |
| 4. fence | 9. terrain | 14. truck |
For the confidence files (_conf.png): PNG format, 16-bits, grayscale, with each pixel's value from 0 to 65535 corresponding to the confidence in the prediction (for the predicted class). Confidences are evaluated on an entire subset of the dataset at once and, thus, are expected to be commensurable across the images of a subset.
Each prediction and confidence image should have exactly the same dimensions as the corresponding input image. The evaluation is made pixel-wise.
The submission files should be assembled on a tarfile or directory tree corresponding to the original hierarchical organization of the full BRAVO Dataset Benchmark (see Appendix 1).
The submission has to be compressed and encoded before being uploaded to the Evaluation Server. This requires using the utilities in this repository.
Clone this repository to local_repo_root and create an environment with the requirements:
cd <local_repo_root>
conda create -n bravo python=3.9
conda activate bravo
python -m pip install -r requirements.txtAlways activate the environment and add local_repo_root to PYTHONPATH before running the commands in the sections below.
conda activate bravo
export PYTHONPATH=<local_repo_root>We use Anaconda in the examples above, but its use is optional. Any manager providing an isolated Python 3.9 environment (e.g., virtualenv) is acceptable.
To encode the submission, you'll need to download the sampling file bravo_SAMPLING.tar.
The submission files must be in a directory tree or in a .tar file. Use one of the commands below:
python -m bravo_toolkit.util.encode_submission <submission-root-directory> <encoded-submission-output.tar> --samples bravo_SAMPLING.taror
python -m bravo_toolkit.util.encode_submission <submission-raw-files.tar> <encoded-submission-output.tar> --samples bravo_SAMPLING.tarWe are excited to unveil the BRAVO Challenge as an initiative within ELSA — European Lighthouse on Secure and Safe AI, a network of excellence funded by the European Union. The BRAVO Challenge is officially featured on the ELSA Benchmarks website as the Autonomous Driving/Robust Perception task.
- Use the task website to upload your submission: https://benchmarks.elsa-ai.eu/?ch=1&com=introduction
- The BRAVO Challenge appears as "Autonomous Driving" on the submission server
- Each team must register one single account on the submission server
- Submissions to the server are initially private and do not appear on the leaderboard. You must edit your submission to make it public before the deadline for it to count for the challenge
Baseline techniques are available as optional inspiration and points of departure for the teams, including:
- Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation (Besnier et al., ICCV 2021)
The code for those baselines, already adapted for the challenge data, is on the baselines/ folder of this code repository.
Each team must submit a technical whitepaper describing the techniques attempted. Teams may choose to describe multiple submissions in a single report or make multiple reports. It should be clear which submissions are described in each report.
Each report must have at most 4500 words (discounting references) and at most 5 floats in any template reasonably resembling a research paper.
We will collect the best results into a summary report, submitted within the scope of the UNCV Workshop, with the authors of the winning submissions as co-authors.
| Date | Event |
|---|---|
| 2024-06-17 Mon | BRAVO Challenge 2024 launched, data and code available for download |
| 2024-07-01 Mon | Submission server open |
| 2024-08-23 Fri | Submission deadline (23:59 CEST) |
| 2024-08-27 Tue | Technical whitepaper deadline (23:59 CEST) |
If you have inquires on the challenge data, code, rules, metrics, etc., please use the issues of this repository. Please check whether your issue (or a sufficiently similar one) has already been answered before opening a new one.
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
@inproceedings{vu2024bravo,
title={The BRAVO Semantic Segmentation Challenge Results in UNCV2024},
author={Vu, Tuan-Hung and Valle, Eduardo and Bursuc, Andrei and Kerssies, Tommie and de Geus, Daan and Dubbelman, Gijs and Qian, Long and Zhu, Bingke and Chen, Yingying and Tang, Ming and Wang, Jinqiao and Vojíř, Tomáš and Šochman, Jan and Matas, Jiří and Smith, Michael and Ferrie, Frank and Basu, Shamik and Sakaridis, Christos and Van Gool, Luc},
booktitle={ECCV},
year={2024}
}
@inproceedings{loiseau2024reliability,
title={Reliability in Semantic Segmentation: Can We Use Synthetic Data?},
author={Loiseau, Thibaut and Vu, Tuan-Hung and Chen, Mickael and P{\'e}rez, Patrick and Cord, Matthieu},
booktitle={ECCV},
year={2024}
}
We extend our heartfelt gratitude to the authors of ACDC, SegmentMeIfYouCan, and Out-of-context Cityscapes for generously permitting us to repurpose their benchmarking data. We are also thankful to the authors of GuidedDisent and Flare Removal for providing the amazing toolboxes that helped synthesize realistic-looking raindrops and light flares. All those people collectively contributed to creating BRAVO, a unified benchmark for robustness in autonomous driving.
The BRAVO Challenge is an initiative within ELSA — European Lighthouse on Secure and Safe AI, a network of excellence funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or ELSA. Neither the European Union nor ELSA can be held responsible for them.
See LICENSE.md.
The submission directory, or raw input tar file expected by encode_submission should have the following structure:
submission_directory_root or submission_raw.tar
├── bravo_ACDC
│ ├── fog
│ │ └── test
│ │ ├── GOPR0475
│ │ │ ├── GOPR0475_frame_000247_rgb_anon_conf.png
│ │ │ ├── GOPR0475_frame_000247_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0475_frame_001060_rgb_anon_conf.png
│ │ │ └── GOPR0475_frame_001060_rgb_anon_pred.png
│ │ ├── GOPR0477
│ │ │ ├── GOPR0477_frame_000794_rgb_anon_conf.png
│ │ │ ├── GOPR0477_frame_000794_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0477_frame_001032_rgb_anon_conf.png
│ │ │ └── GOPR0477_frame_001032_rgb_anon_pred.png
│ │ ├── GOPR0478
│ │ │ ├── GOPR0478_frame_000259_rgb_anon_conf.png
│ │ │ ├── GOPR0478_frame_000259_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0478_frame_001023_rgb_anon_conf.png
│ │ │ └── GOPR0478_frame_001023_rgb_anon_pred.png
│ │ ├── GP010475
│ │ │ ├── GP010475_frame_000006_rgb_anon_conf.png
│ │ │ ├── GP010475_frame_000006_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010475_frame_000831_rgb_anon_conf.png
│ │ │ └── GP010475_frame_000831_rgb_anon_pred.png
│ │ ├── GP010477
│ │ │ ├── GP010477_frame_000001_rgb_anon_conf.png
│ │ │ ├── GP010477_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010477_frame_000224_rgb_anon_conf.png
│ │ │ └── GP010477_frame_000224_rgb_anon_pred.png
│ │ ├── GP010478
│ │ │ ├── GP010478_frame_000032_rgb_anon_conf.png
│ │ │ ├── GP010478_frame_000032_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010478_frame_001061_rgb_anon_conf.png
│ │ │ └── GP010478_frame_001061_rgb_anon_pred.png
│ │ └── GP020478
│ │ ├── GP020478_frame_000001_rgb_anon_conf.png
│ │ ├── GP020478_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP020478_frame_000042_rgb_anon_conf.png
│ │ └── GP020478_frame_000042_rgb_anon_pred.png
│ ├── night
│ │ └── test
│ │ ├── GOPR0355
│ │ │ ├── GOPR0355_frame_000138_rgb_anon_conf.png
│ │ │ ├── GOPR0355_frame_000138_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0355_frame_000214_rgb_anon_conf.png
│ │ │ └── GOPR0355_frame_000214_rgb_anon_pred.png
│ │ ├── GOPR0356
│ │ │ ├── GOPR0356_frame_000065_rgb_anon_conf.png
│ │ │ ├── GOPR0356_frame_000065_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0356_frame_001001_rgb_anon_conf.png
│ │ │ └── GOPR0356_frame_001001_rgb_anon_pred.png
│ │ ├── GOPR0364
│ │ │ ├── GOPR0364_frame_000001_rgb_anon_conf.png
│ │ │ ├── GOPR0364_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0364_frame_001053_rgb_anon_conf.png
│ │ │ └── GOPR0364_frame_001053_rgb_anon_pred.png
│ │ ├── GOPR0594
│ │ │ ├── GOPR0594_frame_000114_rgb_anon_conf.png
│ │ │ ├── GOPR0594_frame_000114_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0594_frame_001060_rgb_anon_conf.png
│ │ │ └── GOPR0594_frame_001060_rgb_anon_pred.png
│ │ ├── GP010364
│ │ │ ├── GP010364_frame_000009_rgb_anon_conf.png
│ │ │ ├── GP010364_frame_000009_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010364_frame_000443_rgb_anon_conf.png
│ │ │ └── GP010364_frame_000443_rgb_anon_pred.png
│ │ └── GP010594
│ │ ├── GP010594_frame_000003_rgb_anon_conf.png
│ │ ├── GP010594_frame_000003_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010594_frame_000087_rgb_anon_conf.png
│ │ └── GP010594_frame_000087_rgb_anon_pred.png
│ ├── rain
│ │ └── test
│ │ ├── GOPR0572
│ │ │ ├── GOPR0572_frame_000145_rgb_anon_conf.png
│ │ │ ├── GOPR0572_frame_000145_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0572_frame_001035_rgb_anon_conf.png
│ │ │ └── GOPR0572_frame_001035_rgb_anon_pred.png
│ │ ├── GOPR0573
│ │ │ ├── GOPR0573_frame_000180_rgb_anon_conf.png
│ │ │ ├── GOPR0573_frame_000180_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GOPR0573_frame_001046_rgb_anon_conf.png
│ │ │ └── GOPR0573_frame_001046_rgb_anon_pred.png
│ │ ├── GP010400
│ │ │ ├── GP010400_frame_000616_rgb_anon_conf.png
│ │ │ ├── GP010400_frame_000616_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010400_frame_001057_rgb_anon_conf.png
│ │ │ └── GP010400_frame_001057_rgb_anon_pred.png
│ │ ├── GP010402
│ │ │ ├── GP010402_frame_000326_rgb_anon_conf.png
│ │ │ ├── GP010402_frame_000326_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010402_frame_001046_rgb_anon_conf.png
│ │ │ └── GP010402_frame_001046_rgb_anon_pred.png
│ │ ├── GP010571
│ │ │ ├── GP010571_frame_000077_rgb_anon_conf.png
│ │ │ ├── GP010571_frame_000077_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010571_frame_001050_rgb_anon_conf.png
│ │ │ └── GP010571_frame_001050_rgb_anon_pred.png
│ │ ├── GP010572
│ │ │ ├── GP010572_frame_000027_rgb_anon_conf.png
│ │ │ ├── GP010572_frame_000027_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010572_frame_000916_rgb_anon_conf.png
│ │ │ └── GP010572_frame_000916_rgb_anon_pred.png
│ │ ├── GP010573
│ │ │ ├── GP010573_frame_000001_rgb_anon_conf.png
│ │ │ ├── GP010573_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP010573_frame_001056_rgb_anon_conf.png
│ │ │ └── GP010573_frame_001056_rgb_anon_pred.png
│ │ ├── GP020400
│ │ │ ├── GP020400_frame_000001_rgb_anon_conf.png
│ │ │ ├── GP020400_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP020400_frame_000142_rgb_anon_conf.png
│ │ │ └── GP020400_frame_000142_rgb_anon_pred.png
│ │ ├── GP020571
│ │ │ ├── GP020571_frame_000001_rgb_anon_conf.png
│ │ │ ├── GP020571_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP020571_frame_000248_rgb_anon_conf.png
│ │ │ └── GP020571_frame_000248_rgb_anon_pred.png
│ │ ├── GP020573
│ │ │ ├── GP020573_frame_000001_rgb_anon_conf.png
│ │ │ ├── GP020573_frame_000001_rgb_anon_pred.png
│ │ │ ├── ...
│ │ │ ├── GP020573_frame_000887_rgb_anon_conf.png
│ │ │ └── GP020573_frame_000887_rgb_anon_pred.png
│ │ └── GP030573
│ │ ├── GP030573_frame_000073_rgb_anon_conf.png
│ │ ├── GP030573_frame_000073_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP030573_frame_000914_rgb_anon_conf.png
│ │ └── GP030573_frame_000914_rgb_anon_pred.png
│ └── snow
│ └── test
│ ├── GOPR0122
│ │ ├── GOPR0122_frame_000651_rgb_anon_conf.png
│ │ ├── GOPR0122_frame_000651_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GOPR0122_frame_001054_rgb_anon_conf.png
│ │ └── GOPR0122_frame_001054_rgb_anon_pred.png
│ ├── GOPR0176
│ │ ├── GOPR0176_frame_000394_rgb_anon_conf.png
│ │ ├── GOPR0176_frame_000394_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GOPR0176_frame_000884_rgb_anon_conf.png
│ │ └── GOPR0176_frame_000884_rgb_anon_pred.png
│ ├── GOPR0494
│ │ ├── GOPR0494_frame_000020_rgb_anon_conf.png
│ │ ├── GOPR0494_frame_000020_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GOPR0494_frame_001056_rgb_anon_conf.png
│ │ └── GOPR0494_frame_001056_rgb_anon_pred.png
│ ├── GOPR0496
│ │ ├── GOPR0496_frame_000663_rgb_anon_conf.png
│ │ ├── GOPR0496_frame_000663_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GOPR0496_frame_001033_rgb_anon_conf.png
│ │ └── GOPR0496_frame_001033_rgb_anon_pred.png
│ ├── GP010122
│ │ ├── GP010122_frame_000001_rgb_anon_conf.png
│ │ ├── GP010122_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010122_frame_000223_rgb_anon_conf.png
│ │ └── GP010122_frame_000223_rgb_anon_pred.png
│ ├── GP010176
│ │ ├── GP010176_frame_000001_rgb_anon_conf.png
│ │ ├── GP010176_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010176_frame_001057_rgb_anon_conf.png
│ │ └── GP010176_frame_001057_rgb_anon_pred.png
│ ├── GP010494
│ │ ├── GP010494_frame_000001_rgb_anon_conf.png
│ │ ├── GP010494_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010494_frame_000242_rgb_anon_conf.png
│ │ └── GP010494_frame_000242_rgb_anon_pred.png
│ ├── GP010496
│ │ ├── GP010496_frame_000001_rgb_anon_conf.png
│ │ ├── GP010496_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010496_frame_000883_rgb_anon_conf.png
│ │ └── GP010496_frame_000883_rgb_anon_pred.png
│ ├── GP010606
│ │ ├── GP010606_frame_000001_rgb_anon_conf.png
│ │ ├── GP010606_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP010606_frame_001054_rgb_anon_conf.png
│ │ └── GP010606_frame_001054_rgb_anon_pred.png
│ ├── GP020176
│ │ ├── GP020176_frame_000001_rgb_anon_conf.png
│ │ ├── GP020176_frame_000001_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP020176_frame_001060_rgb_anon_conf.png
│ │ └── GP020176_frame_001060_rgb_anon_pred.png
│ ├── GP020606
│ │ ├── GP020606_frame_000021_rgb_anon_conf.png
│ │ ├── GP020606_frame_000021_rgb_anon_pred.png
│ │ ├── ...
│ │ ├── GP020606_frame_000558_rgb_anon_conf.png
│ │ └── GP020606_frame_000558_rgb_anon_pred.png
│ └── GP030176
│ ├── GP030176_frame_000001_rgb_anon_conf.png
│ ├── GP030176_frame_000001_rgb_anon_pred.png
│ ├── ...
│ ├── GP030176_frame_000369_rgb_anon_conf.png
│ └── GP030176_frame_000369_rgb_anon_pred.png
├── bravo_SMIYC
│ └── RoadAnomaly21
│ └── images
│ ├── airplane0000_conf.png
│ ├── airplane0000_pred.png
│ ├── ...
│ ├── zebra0001_conf.png
│ └── zebra0001_pred.png
├── bravo_outofcontext
│ ├── frankfurt
│ │ ├── frankfurt_000000_000576_leftImg8bit_conf.png
│ │ ├── frankfurt_000000_000576_leftImg8bit_pred.png
│ │ ├── ...
│ │ ├── frankfurt_000001_082466_leftImg8bit_conf.png
│ │ └── frankfurt_000001_082466_leftImg8bit_pred.png
│ ├── lindau
│ │ ├── lindau_000000_000019_leftImg8bit_conf.png
│ │ ├── lindau_000000_000019_leftImg8bit_pred.png
│ │ ├── ...
│ │ ├── lindau_000058_000019_leftImg8bit_conf.png
│ │ └── lindau_000058_000019_leftImg8bit_pred.png
│ └── munster
│ ├── munster_000000_000019_leftImg8bit_conf.png
│ ├── munster_000000_000019_leftImg8bit_pred.png
│ ├── ...
│ ├── munster_000172_000019_leftImg8bit_conf.png
│ └── munster_000172_000019_leftImg8bit_pred.png
├── bravo_synflare
│ ├── frankfurt
│ │ ├── frankfurt_000000_000294_leftImg8bit_conf.png
│ │ ├── frankfurt_000000_000294_leftImg8bit_pred.png
│ │ ├── ...
│ │ ├── frankfurt_000001_082466_leftImg8bit_conf.png
│ │ └── frankfurt_000001_082466_leftImg8bit_pred.png
│ ├── lindau
│ │ ├── lindau_000000_000019_leftImg8bit_conf.png
│ │ ├── lindau_000000_000019_leftImg8bit_pred.png
│ │ ├── ...
│ │ ├── lindau_000058_000019_leftImg8bit_conf.png
│ │ └── lindau_000058_000019_leftImg8bit_pred.png
│ └── munster
│ ├── munster_000000_000019_leftImg8bit_conf.png
│ ├── munster_000000_000019_leftImg8bit_pred.png
│ ├── ...
│ ├── munster_000172_000019_leftImg8bit_conf.png
│ └── munster_000172_000019_leftImg8bit_pred.png
├── bravo_synobjs
│ ├── armchair
│ │ ├── 1_conf.png
│ │ ├── 1_pred.png
│ │ ├── ...
│ │ ├── 504_conf.png
│ │ ├── 504_pred.png
│ ├── baby
│ │ ├── 49_conf.png
│ │ ├── 49_pred.png
│ │ ├── ...
│ │ ├── 421_conf.png
│ │ ├── 421_pred.png
│ ├── bathtub
│ │ ├── 16_conf.png
│ │ ├── 16_pred.png
│ │ ├── ...
│ │ ├── 501_conf.png
│ │ ├── 501_pred.png
│ ├── bench
│ │ ├── 0_conf.png
│ │ ├── 0_pred.png
│ │ ├── ...
│ │ ├── 423_conf.png
│ │ ├── 423_pred.png
│ ├── billboard
│ │ ├── 134_conf.png
│ │ ├── 134_pred.png
│ │ ├── ...
│ │ ├── 461_conf.png
│ │ └── 461_pred.png
│ ├── box
│ │ ├── 58_conf.png
│ │ ├── 58_pred.png
│ │ ├── ...
│ │ ├── 381_conf.png
│ │ ├── 381_pred.png
│ ├── cheetah
│ │ ├── 14_conf.png
│ │ ├── 14_pred.png
│ │ ├── ...
│ │ ├── 500_conf.png
│ │ ├── 500_pred.png
│ ├── chimpanzee
│ │ ├── 0_conf.png
│ │ ├── 0_pred.png
│ │ ├── ...
│ │ ├── 468_conf.png
│ │ ├── 468_pred.png
│ ├── elephant
│ │ ├── 9_conf.png
│ │ └── 9_pred.png
│ │ ├── ...
│ │ ├── 441_conf.png
│ │ ├── 441_pred.png
│ ├── flamingo
│ │ ├── 5_conf.png
│ │ ├── 5_pred.png
│ │ ├── ...
│ │ ├── 482_conf.png
│ │ ├── 482_pred.png
│ ├── giraffe
│ │ ├── 8_conf.png
│ │ └── 8_pred.png
│ │ ├── ...
│ │ ├── 510_conf.png
│ │ ├── 510_pred.png
│ ├── gorilla
│ │ ├── 4_conf.png
│ │ ├── 4_pred.png
│ │ ├── ...
│ │ ├── 493_conf.png
│ │ ├── 493_pred.png
│ ├── hippopotamus
│ │ ├── 29_conf.png
│ │ ├── 29_pred.png
│ │ ├── ...
│ │ ├── 442_conf.png
│ │ ├── 442_pred.png
│ ├── kangaroo
│ │ ├── 6_conf.png
│ │ ├── 6_pred.png
│ │ ├── ...
│ │ ├── 495_conf.png
│ │ ├── 495_pred.png
│ ├── koala
│ │ ├── 0_conf.png
│ │ ├── 0_pred.png
│ │ ├── ...
│ │ ├── 489_conf.png
│ │ ├── 489_pred.png
│ ├── lion
│ │ ├── 7_conf.png
│ │ ├── 7_pred.png
│ │ ├── ...
│ │ ├── 503_conf.png
│ │ ├── 503_pred.png
│ ├── panda
│ │ ├── 5_conf.png
│ │ ├── 5_pred.png
│ │ ├── ...
│ │ ├── 494_conf.png
│ │ ├── 494_pred.png
│ ├── penguin
│ │ ├── 5_conf.png
│ │ ├── 5_pred.png
│ │ ├── ...
│ │ ├── 465_conf.png
│ │ ├── 465_pred.png
│ ├── plant
│ │ ├── 3_conf.png
│ │ ├── 3_pred.png
│ │ ├── ...
│ │ ├── 400_conf.png
│ │ ├── 400_pred.png
│ ├── polar bear
│ │ ├── 4_conf.png
│ │ ├── 4_pred.png
│ │ ├── ...
│ │ ├── 501_conf.png
│ │ ├── 501_pred.png
│ ├── sofa
│ │ ├── 3_conf.png
│ │ ├── 3_pred.png
│ │ ├── ...
│ │ ├── 453_conf.png
│ │ ├── 453_pred.png
│ ├── table
│ │ ├── 0_conf.png
│ │ ├── 0_pred.png
│ │ ├── ...
│ │ ├── 461_conf.png
│ │ └── 461_pred.png
│ ├── tiger
│ │ ├── 28_conf.png
│ │ ├── 28_pred.png
│ │ ├── ...
│ │ ├── 450_conf.png
│ │ ├── 450_pred.png
│ ├── toilet
│ │ ├── 15_conf.png
│ │ ├── 15_pred.png
│ │ ├── ...
│ │ ├── 504_conf.png
│ │ ├── 504_pred.png
│ ├── vase
│ │ ├── 3_conf.png
│ │ ├── 3_pred.png
│ │ ├── ...
│ │ ├── 506_conf.png
│ │ ├── 506_pred.png
│ └── zebra
│ ├── 5_conf.png
│ ├── 5_pred.png
│ ├── ...
│ ├── 499_conf.png
│ ├── 499_pred.png
└── bravo_synrain
├── frankfurt
│ ├── frankfurt_000000_000294_leftImg8bit_conf.png
│ ├── frankfurt_000000_000294_leftImg8bit_pred.png
│ ├── ...
│ ├── frankfurt_000001_083852_leftImg8bit_conf.png
│ └── frankfurt_000001_083852_leftImg8bit_pred.png
├── lindau
│ ├── lindau_000000_000019_leftImg8bit_conf.png
│ ├── lindau_000000_000019_leftImg8bit_pred.png
│ ├── ...
│ ├── lindau_000058_000019_leftImg8bit_conf.png
│ └── lindau_000058_000019_leftImg8bit_pred.png
└── munster
├── munster_000000_000019_leftImg8bit_conf.png
├── munster_000000_000019_leftImg8bit_pred.png
├── ...
├── munster_000173_000019_leftImg8bit_conf.png
└── munster_000173_000019_leftImg8bit_pred.png
88 directories, 7802 files