Datasets:
Tasks:
Object Detection
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Libraries:
FiftyOne
| 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} | |
| } | |
| ``` | |