README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': brick
            '1': carpet
            '2': ceramic
            '3': fabric
            '4': foliage
            '5': food
            '6': glass
            '7': hair
            '8': leather
            '9': metal
            '10': mirror
            '11': other
            '12': painted
            '13': paper
            '14': plastic
            '15': polishedstone
            '16': skin
            '17': sky
            '18': stone
            '19': tile
            '20': wallpaper
            '21': water
            '22': wood
  splits:
    - name: train
      num_bytes: 2017774670.25
      num_examples: 48875
    - name: test
      num_bytes: 240662928
      num_examples: 5750
    - name: validation
      num_bytes: 135991437.125
      num_examples: 2875
  download_size: 2350770974
  dataset_size: 2394429035.375
license: cc-by-4.0
task_categories:
  - image-classification
language:
  - en
size_categories:
  - 10K<n<100K

Materials in Context Dataset (MINC-2500)

Dataset Description

• Homepage: http://opensurfaces.cs.cornell.edu/publications/minc/
• Paper: https://openaccess.thecvf.com/content_cvpr_2015/html/Bell_Material_Recognition_in_2015_CVPR_paper.html

Dataset Summary

(from the website) MINC-2500 is a patch classification dataset with 2500 samples per category (Section 5.4 of the paper). This is a subset of MINC where samples have been sized to 362 x 362 and each category is sampled evenly. The original resolution images are not needed as we include the extracted patches in the archive.