---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': accordion
          '1': airplanes
          '2': anchor
          '3': ant
          '4': barrel
          '5': bass
          '6': beaver
          '7': binocular
          '8': bonsai
          '9': brain
          '10': brontosaurus
          '11': buddha
          '12': butterfly
          '13': camera
          '14': cannon
          '15': car_side
          '16': ceiling_fan
          '17': cellphone
          '18': chair
          '19': chandelier
          '20': cougar_body
          '21': cougar_face
          '22': crab
          '23': crayfish
          '24': crocodile
          '25': crocodile_head
          '26': cup
          '27': dalmatian
          '28': dollar_bill
          '29': dolphin
          '30': dragonfly
          '31': electric_guitar
          '32': elephant
          '33': emu
          '34': euphonium
          '35': ewer
          '36': faces
          '37': faces_easy
          '38': ferry
          '39': flamingo
          '40': flamingo_head
          '41': garfield
          '42': gerenuk
          '43': gramophone
          '44': grand_piano
          '45': hawksbill
          '46': headphone
          '47': hedgehog
          '48': helicopter
          '49': ibis
          '50': inline_skate
          '51': joshua_tree
          '52': kangaroo
          '53': ketch
          '54': lamp
          '55': laptop
          '56': leopards
          '57': llama
          '58': lobster
          '59': lotus
          '60': mandolin
          '61': mayfly
          '62': menorah
          '63': metronome
          '64': minaret
          '65': motorbikes
          '66': nautilus
          '67': octopus
          '68': okapi
          '69': pagoda
          '70': panda
          '71': pigeon
          '72': pizza
          '73': platypus
          '74': pyramid
          '75': revolver
          '76': rhino
          '77': rooster
          '78': saxophone
          '79': schooner
          '80': scissors
          '81': scorpion
          '82': sea_horse
          '83': snoopy
          '84': soccer_ball
          '85': stapler
          '86': starfish
          '87': stegosaurus
          '88': stop_sign
          '89': strawberry
          '90': sunflower
          '91': tick
          '92': trilobite
          '93': umbrella
          '94': watch
          '95': water_lilly
          '96': wheelchair
          '97': wild_cat
          '98': windsor_chair
          '99': wrench
          '100': yin_yang
  splits:
  - name: train
    num_bytes: 121007587.037
    num_examples: 8677
  download_size: 121217709
  dataset_size: 121007587.037
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: unknown
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Dataset Card for Caltech 101

This dataset contains images of objects from 101 distinct categories, with each category comprising approximately 40 to 800 images. The majority of categories include around 50 images each. The images were collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc’Aurelio Ranzato. Each image has an approximate resolution of 300 x 200 pixels.

### Dataset Sources

- **Website:** https://data.caltech.edu/records/mzrjq-6wc02

## Use in FL

In order to prepare the dataset for the FL settings, we recommend using [Flower Dataset](https://flower.ai/docs/datasets/) (flwr-datasets) for the dataset download and partitioning and [Flower](https://flower.ai/docs/framework/) (flwr) for conducting FL experiments.

To partition the dataset, do the following. 
1. Install the package.
```bash
pip install flwr-datasets[vision]
```
2. Use the HF Dataset under the hood in Flower Datasets.
```python
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import IidPartitioner

fds = FederatedDataset(
    dataset="flwrlabs/caltech101",
    partitioners={"train": IidPartitioner(num_partitions=10)}
)
partition = fds.load_partition(partition_id=0)
```

## Dataset Structure

### Data Instances

The first instance of the train split is presented below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=397x150>,
 'label': 1
}
```
### Data Split

```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 8677
    })
})
```

## Implementation details

Note that in this implementation, the string labels are first transformed into lowercase and then sorted alphabetically before providing the integer mapping. This methodology can vary across implementations. 

## Citation

When working with the Caltech-101 dataset, please cite the original paper. 
If you're using this dataset with Flower Datasets and Flower, cite Flower.

**BibTeX:**

Dataset Bibtex:

```
@misc{li2022caltech,
  title        = {Caltech 101},
  author       = {Li, Fei-Fei and Andreeto, Marco and Ranzato, Marc'Aurelio and Perona, Pietro},
  year         = {2022},
  month        = {Apr},
  publisher    = {CaltechDATA},
  doi          = {10.22002/D1.20086},
  abstract     = {Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a MATLAB script to view the annotations, 'show_annotations.m'.}
}
````

Flower:

```
@article{DBLP:journals/corr/abs-2007-14390,
  author       = {Daniel J. Beutel and
                  Taner Topal and
                  Akhil Mathur and
                  Xinchi Qiu and
                  Titouan Parcollet and
                  Nicholas D. Lane},
  title        = {Flower: {A} Friendly Federated Learning Research Framework},
  journal      = {CoRR},
  volume       = {abs/2007.14390},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.14390},
  eprinttype    = {arXiv},
  eprint       = {2007.14390},
  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

## Dataset Card Contact

If you have any questions about the dataset preprocessing and preparation, please contact [Flower Labs](https://flower.ai/).