Datasets:
Tasks:
Object Detection
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Libraries:
FiftyOne
File size: 3,442 Bytes
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---
annotations_creators: []
language: en
size_categories:
- 10K<n<100K
task_categories:
- object-detection
task_ids: []
pretty_name: LVIS-35k
tags:
- fiftyone
- image
- object-detection
dataset_summary: '
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
## Installation
If you haven''t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include ''max_samples'', etc
dataset = fouh.load_from_hub("Voxel51/LVIS")
# Launch the App
session = fo.launch_app(dataset)
```
'
---
# Dataset Card for LVIS-35k
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
**NOTE:** This is only a 35k sample subset of the full dataset. The notebook recipe for creating this, and the full, dataset can be found [here](https://colab.research.google.com/drive/1SmdZPWtLhNis_cCRnO9WKKZQ9OaP_C_d)
## Installation
If you haven't already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/LVIS")
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
LVIS (pronounced 'el-vis') is a dataset for large vocabulary instance segmentation, introduced by researchers from Facebook AI.
- It contains annotations for over 1000 object categories across 164k images. The full dataset is planned to have ~2 million high-quality instance segmentation masks.
- The categories in LVIS follow a natural long-tail distribution, with a few common categories and many rare ones with few training examples. This long tail poses a challenge for current state-of-the-art object detection methods which struggle with low-sample categories.
- The vocabulary was constructed iteratively, starting from 8.8k concrete noun synsets in WordNet and filtering down to the final set[4].
- LVIS can be used for instance segmentation, semantic segmentation, and object detection tasks. The dataset aims to focus the research community on the open challenge of long-tail object recognition.
In summary, LVIS is a large-scale, high-quality dataset that targets the difficult problem of learning segmentation models for various object categories, including many rare ones. It is freely available for research use.
- **Curated by:** Agrim Gupta, Piotr Dollár, Ross Girshick
- **Funded by:** Facebook AI Research (FAIR)
- **Shared by:** [Harpreet Sahota](twitter.com/datascienceharp), Hacker-in-Residence at Voxel51
- **Language(s) (NLP):** en
- **License:** [Custom License](https://github.com/lvis-dataset/lvis-api/blob/master/LICENSE)
### Dataset Sources [optional]
- **Website:** https://www.lvisdataset.org/
- **Repository:** https://github.com/lvis-dataset/lvis-api
- **Paper:** https://arxiv.org/abs/1908.03195
## Citation
**BibTeX:**
```bibtex
@inproceedings{gupta2019lvis,
title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
year={2019}
}
```
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