https://github.com/prbonn/phenobench-baselines
Baselines of the PhenoBench Dataset
https://github.com/prbonn/phenobench-baselines
Last synced: 9 months ago
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Baselines of the PhenoBench Dataset
- Host: GitHub
- URL: https://github.com/prbonn/phenobench-baselines
- Owner: PRBonn
- Created: 2023-07-15T19:27:25.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-23T10:05:11.000Z (almost 2 years ago)
- Last Synced: 2024-01-23T11:26:48.034Z (almost 2 years ago)
- Language: Python
- Homepage: https://www.phenobench.org
- Size: 434 KB
- Stars: 15
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PhenoBench Baselines
[PhenoBench](https://www.phenobench.org) is a large dataset and benchmarks for the semantic interpretation of images of real agricultural fields. The [benchmark tasks](https://www.phenobench.org/benchmarks.html) cover semantic segmentation of crops and weeds, panoptic segmentation of plants, leaf instance segmentation, detection of plants and leaves, and the novel task of hierarchical panoptic segmentation for jointly identifying plants and leaves.
## Implementations
In this repository, we provide code and instructions to reproduce the baseline results reported in our [paper](https://arxiv.org/pdf/2306.04557.pdf). Please see the sub-folders of the individual tasks for further instructions, configurations, and setup of the baselines.
**Important:** These are copies from the original repositories. We tried to make it transparent where the code originated with a link to the original repository. Thus, leave a :star: at their repository if you use their code.
## Checkpoints
To allow to produce the exact same results as reported in the paper, we also provide pre-trained models for our baseline methods. Thus, one can use the inference code to reproduce the results. For an overview of the pre-trained models, i.e., checkpoints of the final models.
Checkpoints
TaskApproachCheckpoint
Semantic SegmentationERFNetDownload
Semantic SegmentationDeepLabV3+Download
Panoptic SegmentationMask R-CNNDownload
Panoptic SegmentationPanoptic DeepLabDownload
Panoptic SegmentationMask2FormerDownload
Leaf Instance SegmentationMask R-CNNDownload
Leaf Instance SegmentationMask2FormerDownload
Hierarchical Panoptic SegmentationWeyler et al.Download
Hierarchical Panoptic SegmentationHAPTDownload
Plant DetectionFaster R-CNNDownload
Plant DetectionMask R-CNNDownload
Plant DetectionYOLOv7Download
Leaf DetectionFaster R-CNNDownload
Leaf DetectionMask R-CNNDownload
Leaf DetectionYOLOv7Download
## Predictions
For comparison and rendering of detailed results, we also provide the submissions files of the predictions to the individual CodaLab competitions. These can be used with the code in the [PhenoBench development kit](https://www.github.com/PRBonn/phenobench) to visualize the results of the baseline and could be valuable for additional images and qualitative comparisons in papers.
Please refer to the [PhenoBench development kit](https://www.github.com/PRBonn/phenobench) for further details on the visualization of the results and additional information on the usage of the provided tools.
Predictions
TaskApproachValidationTest
Semantic SegmentationERFNetDownloadDownload
Semantic SegmentationDeepLabV3+DownloadDownload
Panoptic SegmentationMask R-CNNDownloadDownload
Panoptic SegmentationPanoptic DeepLabDownloadDownload
Panoptic SegmentationMask2FormerDownloadDownload
Leaf Instance Segmentation Mask R-CNNDownloadDownload
Leaf Instance SegmentationMask2FormerDownloadDownload
Hierarchical Panoptic SegmentationWeyler et al.DownloadDownload
Hierarchical Panoptic SegmentationHAPTDownloadDownload
Plant DetectionFaster R-CNNDownloadDownload
Plant DetectionMask R-CNNDownloadDownload
Plant DetectionYOLOv7DownloadDownload
Leaf DetectionFaster R-CNNDownloadDownload
Leaf DetectionMask R-CNNDownloadDownload
Leaf DetectionYOLOv7DownloadDownload
## License
Please see the licenses of the particular sub-folders.
## Citation
If you use the specific baseline code, please cite the corresponding paper. We added a file `CITATION.md` that includes the suggested BibTeX entry for the baseline code.
If you use our dataset, then you should cite our paper [PDF](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2023arxiv.pdf):
```
@article{weyler2023dataset,
author = {Jan Weyler and Federico Magistri and Elias Marks and Yue Linn Chong and Matteo Sodano
and Gianmarco Roggiolani and Nived Chebrolu and Cyrill Stachniss and Jens Behley},
title = {{PhenoBench --- A Large Dataset and Benchmarks for Semantic Image Interpretation
in the Agricultural Domain}},
journal = {arXiv preprint},
year = {2023}
}
```