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Description:
The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly
occurring in the United Kingdom. Each class consists of between 40 and 258
images. The images have large scale, pose and light variations. In addition,
there are categories that have large variations within the category and several
very similar categories.
The dataset is divided into a training set, a validation set and a test set. The
training set and validation set each consist of 10 images per class (totalling
1020 images each). The test set consists of the remaining 6149 images (minimum
20 per class).
@InProceedings{Nilsback08,author="Nilsback, M-E. and Zisserman, A.",title="Automated Flower Classification over a Large Number of Classes",booktitle="Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing",year="2008",month="Dec"}
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-12-15 UTC."],[],[],null,["# oxford_flowers102\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly\noccurring in the United Kingdom. Each class consists of between 40 and 258\nimages. The images have large scale, pose and light variations. In addition,\nthere are categories that have large variations within the category and several\nvery similar categories.\n\nThe dataset is divided into a training set, a validation set and a test set. The\ntraining set and validation set each consist of 10 images per class (totalling\n1020 images each). The test set consists of the remaining 6149 images (minimum\n20 per class).\n| **Note:** The dataset by default comes with a test size larger than the train size. For more info see this [issue](https://github.com/tensorflow/datasets/issues/3022).\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/oxford-102-flower)\n\n- **Homepage** :\n [https://www.robots.ox.ac.uk/\\~vgg/data/flowers/102/](https://www.robots.ox.ac.uk/%7Evgg/data/flowers/102/)\n\n- **Source code** :\n [`tfds.datasets.oxford_flowers102.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/oxford_flowers102/oxford_flowers102_dataset_builder.py)\n\n- **Versions**:\n\n - **`2.1.1`** (default): No release notes.\n- **Download size** : `328.90 MiB`\n\n- **Dataset size** : `331.34 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 6,149 |\n| `'train'` | 1,020 |\n| `'validation'` | 1,020 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'file_name': Text(shape=(), dtype=string),\n 'image': Image(shape=(None, None, 3), dtype=uint8),\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=102),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|-----------|--------------|-----------------|--------|-------------|\n| | FeaturesDict | | | |\n| file_name | Text | | string | |\n| image | Image | (None, None, 3) | uint8 | |\n| label | ClassLabel | | int64 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('image', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @InProceedings{Nilsback08,\n author = \"Nilsback, M-E. and Zisserman, A.\",\n title = \"Automated Flower Classification over a Large Number of Classes\",\n booktitle = \"Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing\",\n year = \"2008\",\n month = \"Dec\"\n }"]]