Datasets:
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- image-classification
task_ids: []
pretty_name: multimodal_shapes_subset
tags:
- fiftyone
- group
- image-classification
dataset_summary: >-
This is a grouped multimodal [FiftyOne](https://github.com/voxel51/fiftyone)
dataset with 2000 samples, each consisting of an rgb image and LiDAR data. The
samples are labeled as cube or sphere. The classes are perfectly balanced
(1000 cubes, 1000 spheres).
This dataset is used in this project on github:
https://github.com/MatthiasCr/Computer-Vision-Assignment-2/tree/main There is
also explained how to use this dataset, visualize it in fiftyone, and convert
it to a torch dataset.
## Installation
If you haven't already, install FiftyOne:
```bash pip install -U fiftyone ```
## Usage
```python import fiftyone as fo from fiftyone.utils.huggingface import
load_from_hub
# Load the dataset # Note: other available arguments include 'max_samples',
etc dataset = load_from_hub("MatthiasCr/multimodal-shapes-subset")
# Launch the App session = fo.launch_app(dataset) ```
Dataset Card for multimodal_shapes_subset
This is a grouped multimodal FiftyOne dataset with 2000 samples, each consisting of an rgb image and LiDAR data of objects. The samples are labeled as cube or sphere. The classes are perfectly balanced (1000 cubes, 1000 spheres).
This dataset is used in this project on github: https://github.com/MatthiasCr/Computer-Vision-Assignment-2/tree/main. There it is also explained how to use this dataset, visualize it in fiftyone, and convert it to a torch dataset.
Installation
If you haven''t already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include ''max_samples'', etc
dataset = load_from_hub("MatthiasCr/multimodal-shapes-subset")
# Launch the App
session = fo.launch_app(dataset)
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