image
image
labels
class label
11sitting
14using_laptop
7hugging
12sleeping
14using_laptop
12sleeping
4drinking
7hugging
1clapping
3dancing
2cycling
4drinking
1clapping
0calling
12sleeping
4drinking
0calling
8laughing
14using_laptop
14using_laptop
1clapping
5eating
6fighting
9listening_to_music
3dancing
4drinking
2cycling
8laughing
4drinking
3dancing
9listening_to_music
2cycling
11sitting
11sitting
12sleeping
5eating
10running
10running
7hugging
7hugging
0calling
14using_laptop
9listening_to_music
2cycling
12sleeping
7hugging
13texting
12sleeping
14using_laptop
10running
7hugging
0calling
14using_laptop
4drinking
7hugging
2cycling
8laughing
0calling
1clapping
8laughing
13texting
11sitting
8laughing
14using_laptop
12sleeping
9listening_to_music
2cycling
1clapping
13texting
13texting
5eating
2cycling
4drinking
0calling
10running
4drinking
13texting
11sitting
0calling
7hugging
5eating
12sleeping
14using_laptop
14using_laptop
10running
0calling
14using_laptop
14using_laptop
9listening_to_music
0calling
3dancing
8laughing
2cycling
1clapping
14using_laptop
0calling
3dancing
4drinking
13texting
6fighting

Dataset Summary

A dataset from kaggle. origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data

Introduction

  • The dataset features 15 different classes of Human Activities.
  • The dataset contains about 12k+ labelled images including the validation images.
  • Each image has only one human activity category and are saved in separate folders of the labelled classes

PROBLEM STATEMENT

  • Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.
  • Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.
  • Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.

About Files

  • Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’ which contain the images of the respective human activities.
  • Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’.
  • Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.
  • sample_submission: This is a csv file that contains the sample submission for the data sprint.

Data Fields

The data instances have the following fields:

  • image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
  • labels: an int classification label. All test data is labeled 0.

Class Label Mappings:

{
    'calling': 0,
    'clapping': 1,
    'cycling': 2,
    'dancing': 3,
    'drinking': 4,
    'eating': 5,
    'fighting': 6,
    'hugging': 7,
    'laughing': 8,
    'listening_to_music': 9,
    'running': 10,
    'sitting': 11,
    'sleeping': 12,
    'texting': 13,
    'using_laptop': 14
}

Data Splits

train test
# of examples 12600 5400

Data Size

  • download: 311.96 MiB
  • generated: 312.59 MiB
  • total: 624.55 MiB
>>> from datasets import load_dataset

>>> ds = load_dataset("Bingsu/Human_Action_Recognition")
>>> ds
DatasetDict({
    test: Dataset({
        features: ['image', 'labels'],
        num_rows: 5400
    })
    train: Dataset({
        features: ['image', 'labels'],
        num_rows: 12600
    })
})

>>> ds["train"].features
{'image': Image(decode=True, id=None),
 'labels': ClassLabel(num_classes=15, names=['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'], id=None)}
 
>>> ds["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>,
 'labels': 11}
Downloads last month
98
Edit dataset card

Models trained or fine-tuned on Bingsu/Human_Action_Recognition