| # Dronescapes Experts dataset | |
| This dataset is an extension of the original [dronescapes dataset](https://huggingface.co/dataset/Meehai/dronescapes) with new modalities generated using VRE 100% from scratch (aka pretrained experts). The only data that is not generable by VRE is the Ground Truth: semantic (human annotated), depth & normals (SfM) that is inherited from the original dataset for evaluation purposes only. | |
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| # 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 | |
| ``` | |
| ## Option 2. Generate all the modalities from raw videos | |
| Follow the instructions under [this file](./vre_dronescapes/commands.txt). | |
| Note: you can generate all the data except `semantic_segprop8` (human annotated), `depth_sfm_manual202204` and | |
| `normals_sfm_manual202204` (SfM tool was used). | |
| ## 2. Using the data | |
| As per the split from the paper: | |
| <img src="split.png", width="500px"> | |
| The data is in `data/*` (if you used git clone) (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](scripts/dronescapes_viewer/dronescapes_viewer.ipynb). 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 VRE reader as well: | |
| ```bash | |
| vre_reader data/test_set_annotated_only/ --config_path vre_dronescapes/cfg.yaml -I vre_dronescapes/semantic_mapper.py:get_new_semantic_mapped_tasks | |
| ``` | |
| ## 3. Evaluation | |
| See the original [dronescapes evaluation description & benchmark](https://huggingface.co/datasets/Meehai/dronescapes#3-evaluation-for-semantic-segmentation) for this. | |