Datasets:
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---
dataset_info:
features:
- name: image
dtype: image
- name: size
dtype: string
- name: objects
dtype: string
- name: positive_captions
dtype: string
- name: negative_captions
dtype: string
- name: ambiguous_captions
dtype: string
- name: positive_objects
dtype: string
- name: negative_objects
dtype: string
- name: ambiguous_objects
dtype: string
splits:
- name: train
num_bytes: 6424085843.442
num_examples: 38118
- name: test
num_bytes: 1683318047.872
num_examples: 9658
download_size: 8236094882
dataset_size: 8107403891.314
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit
task_categories:
- object-detection
- image-feature-extraction
- image-to-text
language:
- en
pretty_name: HICO-DET Dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for HICO-DET Dataset
## Dataset Summary
HICO-DET is a dataset for detecting human-object interactions (HOI) in images. It contains 47,776 images (38,118 in train set and 9,658 in test set), 600 HOI categories constructed by 80 object categories and 117 verb classes. HICO-DET provides more than 150k annotated human-object pairs. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects.
- 47,776 images (38,118 in train set, 9,658 in test set)
- 600 HOI categories
- 80 object categories
- 117 verb classes
- Over 150,000 annotated human-object pairs
Originally hosted at http://www-personal.umich.edu/~ywchao/hico/, the dataset is no longer available at its official website. This Hugging Face version is a converted and restructured copy of the original dataset, which can still be found on [Google Drive](https://drive.google.com/file/d/1A1AOgIucGOkprASRBMxAPNTrXlmadYXE/view?usp=sharing) in MATLAB format.
## Dataset Structure
The dataset is structured as follows:
```
DatasetDict({
train: Dataset({
features: ['image', 'size', 'objects', 'positive_captions', 'negative_captions', 'ambiguous_captions', 'positive_objects', 'negative_objects', 'ambiguous_objects'],
num_rows: 38118
})
test: Dataset({
features: ['image', 'size', 'objects', 'positive_captions', 'negative_captions', 'ambiguous_captions', 'positive_objects', 'negative_objects', 'ambiguous_objects'],
num_rows: 9658
})
})
```
Here is the description of each column:
- `image`: the image
- `size`: the size of the image
- `objects`: the object categories in the image
- `positive_captions`: the positive captions for the image, e.g., ('cake', 'carry') which means the image is a picture of a person carrying a cake. One image can have multiple positive captions.
- `negative_captions`: the negative captions for the image. One image can have multiple negative captions.
- `ambiguous_captions`: the ambiguous captions for the image. One image can have multiple ambiguous captions.
- `positive_objects`: the positive objects for the image. Positive objects are the index of (object, verb) pairs in `list_action.csv`.
- `negative_objects`: the negative objects for the image. Negative objects are the index of (object, verb) pairs in `list_action.csv`.
- `ambiguous_objects`: the ambiguous objects for the image. Ambiguous objects are the index of (object, verb) pairs in `list_action.csv`.
list_action.csv is a csv file that contains the list of (object, verb) pairs and some other useful information, which can be found [here](https://huggingface.co/datasets/zhimeng/hico_det/blob/main/list_action.csv).
## Usage
The most simple usage is to load the dataset with hugging face datasets.
```python
from datasets import load_dataset
dataset = load_dataset("zhimeng/hico_det")
print(dataset)
```
## File Structure
I also provide the original data structure as the following:
```
data/
list_action.csv # 600 HOI categories
images/
train/
metadata.json # 38,118 images
img_00001.jpg
img_00002.jpg
...
test/
metadata.json # 9,658 images
img_00001.jpg
img_00002.jpg
...
```
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