Datasets:

KRadim
/

AwA-Pose-Lite

license: other
license_name: readmefile
license_link: LICENSE
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: id
      dtype: uint32
    - name: right_eye
      large_list: float64
    - name: right_earbase
      large_list: float64
    - name: right_earend
      large_list: float64
    - name: right_antler_base
      large_list: float64
    - name: right_antler_end
      large_list: float64
    - name: left_antler_base
      large_list: float64
    - name: left_antler_end
      large_list: float64
    - name: left_earbase
      large_list: float64
    - name: left_earend
      large_list: float64
    - name: left_eye
      large_list: float64
    - name: nose
      large_list: float64
    - name: upper_jaw
      large_list: float64
    - name: lower_jaw
      large_list: float64
    - name: mouth_end_right
      large_list: float64
    - name: throat_base
      large_list: float64
    - name: neck_base
      large_list: float64
    - name: neck_end
      large_list: float64
    - name: back_base
      large_list: float64
    - name: back_middle
      large_list: float64
    - name: back_end
      large_list: float64
    - name: tail_base
      large_list: float64
    - name: body_middle_right
      large_list: float64
    - name: bbox
      large_list: float64
    - name: mouth_end_left
      large_list: float64
    - name: throat_end
      large_list: float64
    - name: tail_end
      large_list: float64
    - name: front_left_thai
      large_list: float64
    - name: front_left_knee
      large_list: float64
    - name: front_left_paw
      large_list: float64
    - name: front_right_thai
      large_list: float64
    - name: front_right_paw
      large_list: float64
    - name: front_right_knee
      large_list: float64
    - name: back_left_knee
      large_list: float64
    - name: back_left_paw
      large_list: float64
    - name: back_left_thai
      large_list: float64
    - name: back_right_thai
      large_list: float64
    - name: back_right_paw
      large_list: float64
    - name: back_right_knee
      large_list: float64
    - name: belly_bottom
      large_list: float64
    - name: body_middle_left
      large_list: float64
    - name: name_file
      dtype: large_string
    - name: name_class
      dtype: large_string
    - name: image_base64s
      dtype: large_string
    - name: image_width
      dtype: int64
    - name: image_height
      dtype: int64
    - name: image_license
      dtype: large_string
  splits:
    - name: train
      num_bytes: 1557452015
      num_examples: 9423
    - name: validation
      num_bytes: 82813875
      num_examples: 524
    - name: test
      num_bytes: 87294963
      num_examples: 524
  download_size: 1715582665
  dataset_size: 1727560853
task_categories:
  - feature-extraction
tags:
  - biology
pretty_name: AwA-Pose-Lite
size_categories:
  - 10K<n<100K

Dataset Summary

AwA Pose, a quadrupedal keypoint detection dataset that offers richer annotations and greater species diversity than existing datasets. The data supports efficiency, advances in research on generalized keypoint detection in animals.

The dataset was explored, filtered, and stored in a pyarrow type. See : AwA2_dataset_analysis

Original Kaggle dataset: AwA2_dataset

The original data comes from two sources and is described in:

The dataset contains:

Lite version of keypoints dataset
Full version of keypoints dataset

All version contains:

train dataset: 90%
validation dataset: 5%
test dataset: 5%

Description of data in the dataset

Class names:

antelope
bobcat
buffalo
chihuahua
collie
cow
dalmatian
deer
elephant
fox
german+shepherd
giant+panda
giraffe
grizzly+bear
hippopotamus
horse
leopard
lion
moose
otter
ox
persian+cat
pig
polar+bear
rabbit
raccoon
rhinoceros
sheep
siamese+cat
squirrel
tiger
weasel
wolf
zebra

Columns description

name column	description of column
id	id number of records
right_eye	keypoint values [x,y]
right_earbase	keypoint values [x,y]
right_earend	keypoint values [x,y]
right_antler_base	keypoint values [x,y]
right_antler_end	keypoint values [x,y]
left_antler_base	keypoint values [x,y]
left_antler_end	keypoint values [x,y]
left_earbase	keypoint values [x,y]
left_earend	keypoint values [x,y]
left_eye	keypoint values [x,y]
nose	keypoint values [x,y]
upper_jaw	keypoint values [x,y]
lower_jaw	keypoint values [x,y]
mouth_end_right	keypoint values [x,y]
throat_base	keypoint values [x,y]
neck_base	keypoint values [x,y]
neck_end	keypoint values [x,y]
back_base	keypoint values [x,y]
back_middle	keypoint values [x,y]
back_end	keypoint values [x,y]
tail_base	keypoint values [x,y]
body_middle_right	keypoint values [x,y]
bbox	bounding box dimension [x1, y1, x2, y2]
mouth_end_left	keypoint values [x,y]
throat_end	keypoint values [x,y]
tail_end	keypoint values [x,y]
front_left_thai	keypoint values [x,y]
front_left_knee	keypoint values [x,y]
front_left_paw	keypoint values [x,y]
front_right_thai	keypoint values [x,y]
front_right_paw	keypoint values [x,y]
front_right_knee	keypoint values [x,y]
back_left_knee	keypoint values [x,y]
back_left_paw	keypoint values [x,y]
back_left_thai	keypoint values [x,y]
back_right_thai	keypoint values [x,y]
back_right_paw	keypoint values [x,y]
back_right_knee	keypoint values [x,y]
belly_bottom	keypoint values [x,y]
body_middle_left	keypoint values [x,y]
name_file	name of file as string
name_class	name of class as string
image_base64s	image as string Base64
image_width	value of with of image
image_height	value of height of image
image_license	text of licence of image

Notes on data

If a keypoints contains [-1.0, -1.0], it means that the point is not visible in the image. These points must be masked when training the model.

Images are stored as a Base64 string. They can be transformed using the function:

import base64
import io
from PIL import Image

def base64_to_img(base64_str):
    img_bytes = base64.b64decode(base64_str)
    img_buffer = io.BytesIO(img_bytes)
    img = Image.open(img_buffer)
    
    return img

Find out more in AwA2_dataset_analysis

Licensing Information

Data for keypoints is licensed according to GitHub: prinik/AwA-Pose, this license is MIT.

The license for the images is according to Animals with Attributes 2, see data column image_license .