Datasets:
File size: 7,386 Bytes
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---
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](https://arrow.apache.org/docs/python/index.html). See : [AwA2_dataset_analysis](https://www.kaggle.com/code/radimkzl/awa2-dataset-analysis)
***Original Kaggle dataset:*** [AwA2_dataset](https://www.kaggle.com/datasets/radimkzl/awa2-dataset)
The original data comes from two sources and is described in:
- [A Novel Dataset for Keypoint Detection of
Quadruped Animals from Images](https://arxiv.org/pdf/2108.13958)
- [GitHub: prinik/AwA-Pose](https://github.com/prinik/AwA-Pose/tree/main)
- [Animals with Attributes 2](https://cvml.ista.ac.at/AwA2/)
*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:
```python
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](https://www.kaggle.com/code/radimkzl/awa2-dataset-analysis#Create-AwA2-dataset-for-Hugging-Face...)
## **Licensing Information**
Data for keypoints is licensed according to [GitHub: prinik/AwA-Pose](https://github.com/prinik/AwA-Pose/tree/main), this license is [MIT](https://github.com/prinik/AwA-Pose/tree/main?tab=MIT-1-ov-file#readme).
The license for the images is according to [Animals with Attributes 2](https://cvml.ista.ac.at/AwA2/), see data column `image_license `.
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