{
  "citation": "@article{FeiFei2004LearningGV,\n  title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},\n  author={Li Fei-Fei and Rob Fergus and Pietro Perona},\n  journal={Computer Vision and Pattern Recognition Workshop},\n  year={2004},\n}",
  "description": "Caltech-101 consists of pictures of objects belonging to 101 classes, plus one\n`background clutter` class. Each image is labelled with a single object. Each\nclass contains roughly 40 to 800 images, totalling around 9k images. Images are\nof variable sizes, with typical edge lengths of 200-300 pixels. This version\ncontains image-level labels only. The original dataset also contains bounding\nboxes.",
  "downloadSize": "137414764",
  "fileFormat": "tfrecord",
  "location": {
    "urls": [
      "https://doi.org/10.22002/D1.20086"
    ]
  },
  "moduleName": "tensorflow_datasets.datasets.caltech101.caltech101_dataset_builder",
  "name": "caltech101",
  "releaseNotes": {
    "3.0.0": "New split API (https://tensorflow.org/datasets/splits)",
    "3.0.1": "Website URL update",
    "3.0.2": "Download URL update"
  },
  "splits": [
    {
      "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}",
      "name": "train",
      "numBytes": "44481158",
      "shardLengths": [
        "3060"
      ]
    },
    {
      "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}",
      "name": "test",
      "numBytes": "94837659",
      "shardLengths": [
        "6084"
      ]
    }
  ],
  "supervisedKeys": {
    "tuple": {
      "items": [
        {
          "featureKey": "image"
        },
        {
          "featureKey": "label"
        }
      ]
    }
  },
  "version": "3.0.2"
}