README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_coco_url
      dtype: string
    - name: image_date_captured
      dtype: string
    - name: image_file_name
      dtype: string
    - name: image_height
      dtype: int32
    - name: image_width
      dtype: int32
    - name: image_id
      dtype: int32
    - name: image_license
      dtype: int8
    - name: image_open_images_id
      dtype: string
    - name: annotations_ids
      sequence: int32
    - name: annotations_captions
      sequence: string
  splits:
    - name: validation
      num_bytes: 1421862846
      num_examples: 4500
    - name: test
      num_bytes: 3342844310
      num_examples: 10600
  download_size: 4761076789
  dataset_size: 4764707156
configs:
  - config_name: default
    data_files:
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets

This Dataset

This is a formatted version of NoCaps. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.

@inproceedings{Agrawal_2019,
   title={nocaps: novel object captioning at scale},
   url={http://dx.doi.org/10.1109/ICCV.2019.00904},
   DOI={10.1109/iccv.2019.00904},
   booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
   publisher={IEEE},
   author={Agrawal, Harsh and Desai, Karan and Wang, Yufei and Chen, Xinlei and Jain, Rishabh and Johnson, Mark and Batra, Dhruv and Parikh, Devi and Lee, Stefan and Anderson, Peter},
   year={2019},
   month=oct }