|
--- |
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size_categories: |
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- 10K<n<100K |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: left |
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dtype: int64 |
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- name: forward |
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dtype: int64 |
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- name: right |
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dtype: int64 |
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splits: |
|
- name: train |
|
num_bytes: 561073580.031 |
|
num_examples: 12489 |
|
- name: test |
|
num_bytes: 60984278.384 |
|
num_examples: 1388 |
|
download_size: 618329781 |
|
dataset_size: 622057858.415 |
|
configs: |
|
- config_name: default |
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data_files: |
|
- split: train |
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path: data/train-* |
|
- split: test |
|
path: data/test-* |
|
--- |
|
# Image Dataset of Cyberzoo at Delft University of Technology |
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This dataset includes images taken in the Cyberzoo in the aircraft hall of the Delft University of Technology. The dataset consists of both real images (82%) and simulator images (18%). |
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## Dataset Details |
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### Dataset Description |
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This dataset was collected throughout multiple testing sessions at the Cyberzoo, both while actually flying and handheld. The labeling of the data has been performed using monocular depth maps, generated using [Depth-Anything](https://github.com/LiheYoung/Depth-Anything). The exact labeling process has been explained in [this](https://github.com/Timdnb/CNN-for-Micro-Air-Vehicles/blob/main/Dataset_generation.ipynb) notebook. |
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- **Curated by:** [Tim den Blanken](https://github.com/Timdnb) |
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## Uses |
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This dataset can be used to train Convolutional Neural Networks for obstacle avoidance of Micro Air Vehicles in the Cyberzoo of Delft University of Technology. For the entire training pipeline, please go this [this](https://github.com/Timdnb/CNN-for-Micro-Air-Vehicles) repository. |
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## Dataset Structure |
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The dataset consists of a train set (90% of the data) and a test set (10% of the data). Each image has its label embedded in the metadata, the possible labels are: "left", "forward", "right", corresponding to the direction the drone should rotate or fly in. |
