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---
size_categories:
- 10K<n<100K
paperswithcode_id: spair-71k
tags:
- semantic-correspondence
dataset_info:
- config_name: data
features:
- name: img
dtype: image
- name: name
dtype: string
- name: segmentation
dtype: image
- name: filename
dtype: string
- name: src_database
dtype: string
- name: src_annotation
dtype: string
- name: src_image
dtype: string
- name: image_width
dtype: uint32
- name: image_height
dtype: uint32
- name: image_depth
dtype: uint32
- name: category
dtype: string
- name: category_id
dtype:
class_label:
names:
'0': cat
'1': pottedplant
'2': train
'3': bicycle
'4': car
'5': bus
'6': aeroplane
'7': dog
'8': bird
'9': chair
'10': motorbike
'11': cow
'12': bottle
'13': person
'14': boat
'15': sheep
'16': horse
'17': tvmonitor
- name: pose
dtype: string
- name: truncated
dtype: uint8
- name: occluded
dtype: uint8
- name: difficult
dtype: uint8
- name: bndbox
sequence:
dtype: uint32
length: 4
- name: kps
dtype:
array2_d:
dtype: int32
shape:
- None
- 2
- name: azimuth_id
dtype: uint8
splits:
- name: train
num_bytes: 2016911
num_examples: 1800
download_size: 226961117
dataset_size: 2016911
- config_name: pairs
features:
- name: pair_id
dtype: uint32
- name: src_img
dtype: image
- name: src_segmentation
dtype: image
- name: src_data_index
dtype: uint32
- name: src_name
dtype: string
- name: src_imsize
sequence:
dtype: uint32
length: 3
- name: src_bndbox
sequence:
dtype: uint32
length: 4
- name: src_pose
dtype:
class_label:
names:
'0': Unspecified
'1': Frontal
'2': Left
'3': Rear
'4': Right
- name: src_kps
dtype:
array2_d:
dtype: uint32
shape:
- None
- 2
- name: trg_img
dtype: image
- name: trg_segmentation
dtype: image
- name: trg_data_index
dtype: uint32
- name: trg_name
dtype: string
- name: trg_imsize
sequence:
dtype: uint32
length: 3
- name: trg_bndbox
sequence:
dtype: uint32
length: 4
- name: trg_pose
dtype:
class_label:
names:
'0': Unspecified
'1': Frontal
'2': Left
'3': Rear
'4': Right
- name: trg_kps
dtype:
array2_d:
dtype: uint32
shape:
- None
- 2
- name: kps_ids
sequence:
dtype: uint32
- name: category
dtype: string
- name: category_id
dtype:
class_label:
names:
'0': cat
'1': pottedplant
'2': train
'3': bicycle
'4': car
'5': bus
'6': aeroplane
'7': dog
'8': bird
'9': chair
'10': motorbike
'11': cow
'12': bottle
'13': person
'14': boat
'15': sheep
'16': horse
'17': tvmonitor
- name: viewpoint_variation
dtype: uint8
- name: scale_variation
dtype: uint8
- name: truncation
dtype: uint8
- name: occlusion
dtype: uint8
splits:
- name: train
num_bytes: 55925012
num_examples: 53340
- name: validation
num_bytes: 5700112
num_examples: 5384
- name: test
num_bytes: 12931172
num_examples: 12234
download_size: 226961117
dataset_size: 74556296
---
# SPair-71k
This is an inofficial dataset loading script for the semantic correspondence dataset [SPair-71k](https://cvlab.postech.ac.kr/research/SPair-71k/). It downloads the data from the original source and converts it to the huggingface format.
## Dataset Description
- **Homepage:** [SPair-71k dataset](https://cvlab.postech.ac.kr/research/SPair-71k/)
- **Paper:** [SPair-71k: A Large-scale Benchmark for Semantic Correspondence](https://arxiv.org/abs/1908.10543)
## Official Description
Establishing visual correspondences under large intra-class variations, which is often referred to as semantic correspondence or semantic matching, remains a challenging problem in computer vision. Despite its significance, however, most of the datasets for semantic correspondence are limited to a small amount of image pairs with similar viewpoints and scales. In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. We believe this dataset will provide a reliable testbed to study the problem of semantic correspondence and will help to advance research in this area. We provide the results of recent methods on our new dataset as baselines for further research.
## Usage
```python
from datasets import load_dataset
# load image pairs for the semantic correspondence task
pairs = load_dataset("0jl/SPair-71k", trust_remote_code=True)
print('available splits:', pairs.keys()) # train, validation, test
print('exemplary sample:', pairs['train'][0])
# load only the data, i.e. images, segmentation and annotations
data = load_dataset("0jl/SPair-71k", "data", trust_remote_code=True)
print('available splits:', data.keys()) # only train, as the data is used in all splits
print('exemplary sample:', data['train'][0])
```
Output:
```
available splits: dict_keys(['train', 'validation', 'test'])
exemplary sample: {'pair_id': 35987, 'src_img': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at ...>, 'src_segmentation': <PIL.PngImagePlugin.PngImageFile image mode=L size=500x333 at ...>, 'src_data_index': 1017, 'src_name': 'horse/2009_003294', 'src_imsize': [500, 333, 3], 'src_bndbox': [207, 38, 393, 310], 'src_pose': 0, 'src_kps': [[239, 42], [212, 45], [206, 100], [228, 51], [316, 265], [297, 259]], 'trg_img': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at ...>, 'trg_segmentation': <PIL.PngImagePlugin.PngImageFile image mode=L size=500x375 at ...>, 'trg_data_index': 85, 'trg_name': 'horse/2007_006134', 'trg_imsize': [500, 375, 3], 'trg_bndbox': [125, 99, 442, 318], 'trg_pose': 4, 'trg_kps': [[401, 101], [385, 98], [408, 204], [380, 112], [138, 247], [160, 246]], 'kps_ids': [2, 3, 8, 9, 18, 19], 'category': 16, 'viewpoint_variation': 2, 'scale_variation': 0, 'truncation': 0, 'occlusion': 1}
available splits: dict_keys(['train'])
exemplary sample: {'img': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x332 at ...>, 'segmentation': <PIL.PngImagePlugin.PngImageFile image mode=L size=500x332 at ...>, 'annotation': '{"filename": "2007_000175.jpg", "src_database": "The VOC2007 Database", "src_annotation": "PASCAL VOC2007", "src_image": "flickr", "image_width": 500, "image_height": 332, "image_depth": 3, "category": "sheep", "pose": "Unspecified", "truncated": 0, "occluded": 0, "difficult": 0, "bndbox": [25, 34, 419, 271], "kps": {"0": [211, 100], "1": [347, 87], "2": [132, 99], "3": [423, 72], "4": [245, 91], "5": [329, 88], "6": [282, 137], "7": [304, 138], "8": [295, 162], "9": null, "10": null, "11": null, "12": null, "13": null, "14": null, "15": [173, 247], "16": null, "17": [45, 205], "18": null, "19": null, "20": null, "21": null, "22": null, "23": null, "24": null, "25": null, "26": null, "27": null, "28": null, "29": null}, "azimuth_id": 7}', 'name': 'sheep/2007_000175'}
```
## Featues for `pairs` (default)
```python
pairs = load_dataset("0jl/SPair-71k", name='pairs', trust_remote_code=True)
```
splits: `train`, `validation`, `test`
- `pair_id`: `Value(dtype='uint32')`
- `src_img`: `Image(decode=True)`
- `src_segmentation`: `Image(decode=True)`
- `src_data_index`: `Value(dtype='uint32')`
- `src_name`: `Value(dtype='string')`
- `src_imsize`: `Sequence(feature=Value(dtype='uint32'), length=3)`
- `src_bndbox`: `Sequence(feature=Value(dtype='uint32'), length=4)`
- `src_pose`: `ClassLabel(names=['Unspecified', 'Frontal', 'Left', 'Rear', 'Right'])`
- `src_kps`: `Array2D(shape=(None, 2), dtype='uint32')`
- `trg_img`: `Image(decode=True)`
- `trg_segmentation`: `Image(decode=True)`
- `trg_data_index`: `Value(dtype='uint32')`
- `trg_name`: `Value(dtype='string')`
- `trg_imsize`: `Sequence(feature=Value(dtype='uint32'), length=3)`
- `trg_bndbox`: `Sequence(feature=Value(dtype='uint32'), length=4)`
- `trg_pose`: `ClassLabel(names=['Unspecified', 'Frontal', 'Left', 'Rear', 'Right'])`
- `trg_kps`: `Array2D(shape=(None, 2), dtype='uint32')`
- `kps_ids`: `Sequence(feature=Value(dtype='uint32'), length=-1)`
- `category`: `Value(dtype='string')`
- `category_id`: `ClassLabel(names=['cat', 'pottedplant', 'train', 'bicycle', 'car', 'bus', 'aeroplane', 'dog', 'bird', 'chair', 'motorbike', 'cow', 'bottle', 'person', 'boat', 'sheep', 'horse', 'tvmonitor'])`
- `viewpoint_variation`: `Value(dtype='uint8')`
- `scale_variation`: `Value(dtype='uint8')`
- `truncation`: `Value(dtype='uint8')`
- `occlusion`: `Value(dtype='uint8')`
## Features for `data`
```python
data = load_dataset("0jl/SPair-71k", name='data', trust_remote_code=True)
```
splits: `train`
- `img`: `Image(decode=True)`
- `name`: `Value(dtype='string')`
- `segmentation`: `Image(decode=True)`
- `filename`: `Value(dtype='string')`
- `src_database`: `Value(dtype='string')`
- `src_annotation`: `Value(dtype='string')`
- `src_image`: `Value(dtype='string')`
- `image_width`: `Value(dtype='uint32')`
- `image_height`: `Value(dtype='uint32')`
- `image_depth`: `Value(dtype='uint32')`
- `category`: `Value(dtype='string')`
- `category_id`: `ClassLabel(names=['cat', 'pottedplant', 'train', 'bicycle', 'car', 'bus', 'aeroplane', 'dog', 'bird', 'chair', 'motorbike', 'cow', 'bottle', 'person', 'boat', 'sheep', 'horse', 'tvmonitor'])`
- `pose`: `Value(dtype='string')`
- `truncated`: `Value(dtype='uint8')`
- `occluded`: `Value(dtype='uint8')`
- `difficult`: `Value(dtype='uint8')`
- `bndbox`: `Sequence(feature=Value(dtype='uint32'), length=4)`
- `kps`: `Array2D(shape=(None, 2), dtype='int32')`
- `azimuth_id`: `Value(dtype='uint8')`
## Terms of Use
The SPair-71k data includes images and metadata obtained from the [PASCAL-VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [flickr](https://www.flickr.com/) website. Use of these images and metadata must respect the corresponding [terms of use](https://www.flickr.com/help/terms).
## Citation of original dataset
```bibtex
@article{min2019spair,
title={SPair-71k: A Large-scale Benchmark for Semantic Correspondence},
author={Juhong Min and Jongmin Lee and Jean Ponce and Minsu Cho},
journal={arXiv prepreint arXiv:1908.10543},
year={2019}
}
```
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