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--- |
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license: cc-by-4.0 |
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dataset_info: |
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- config_name: semantic-segmentation |
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features: |
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- name: image |
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dtype: image |
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- name: segmentation_mask |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 20684093931 |
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num_examples: 677 |
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dataset_size: 20684093931 |
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download_size: 20650350872 |
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- config_name: animal-category-anomalies |
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features: |
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- name: crop |
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dtype: image |
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- name: animal_category |
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dtype: |
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class_label: |
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names: |
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'0': chicken |
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'1': duck |
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'2': rooster |
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splits: |
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- name: train |
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num_bytes: 1925963361.27 |
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num_examples: 1270 |
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download_size: 1847129073 |
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dataset_size: 1925963361.27 |
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- config_name: instance-segmentation |
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features: |
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- name: image |
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dtype: image |
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- name: instances |
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list: |
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- name: instance_mask |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 20716569484 |
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num_examples: 677 |
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download_size: 20653857847 |
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dataset_size: 20716569484 |
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- config_name: full-dataset |
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features: |
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- name: image |
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dtype: image |
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- name: segmentation_mask |
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dtype: image |
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- name: coop |
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dtype: |
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class_label: |
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names: |
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'0': '1' |
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'1': '2' |
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'2': '3' |
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'3': '4' |
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'4': '5' |
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'5': '6' |
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'6': '7' |
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'7': '8' |
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'8': '9' |
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'9': '10' |
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'10': '11' |
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- name: instances |
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list: |
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- name: crop |
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dtype: image |
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- name: instance_mask |
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dtype: image |
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- name: identity |
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dtype: |
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class_label: |
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names: |
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'0': Beate |
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'1': Borghild |
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'2': Eleonore |
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'3': Mona |
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'4': Henriette |
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'5': Margit |
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'6': Millie |
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'7': Sigrun |
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'8': Kristina |
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'9': Unknown |
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'10': Tina |
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'11': Gretel |
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'12': Lena |
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'13': Yolkoono |
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'14': Skimmy |
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'15': Mavi |
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'16': Mirmir |
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'17': Nugget |
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'18': Fernanda |
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'19': Isolde |
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'20': Mechthild |
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'21': Brunhilde |
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'22': Spiderman |
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'23': Brownie |
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'24': Camy |
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'25': Samy |
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'26': Yin |
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'27': Yuriko |
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'28': Renate |
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'29': Regina |
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'30': Monika |
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'31': Heidi |
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'32': Erna |
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'33': Marina |
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'34': Kathrin |
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'35': Isabella |
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'36': Amalia |
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'37': Edeltraut |
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'38': Erdmute |
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'39': Oktavia |
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'40': Siglinde |
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'41': Ulrike |
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'42': Hermine |
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'43': Matilda |
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'44': Chantal |
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'45': Chayenne |
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'46': Jaqueline |
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'47': Mandy |
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'48': Henny |
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'49': Shady |
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'50': Shorty |
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'51': Evelyn |
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'52': Marley |
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'53': Elvis |
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'54': Jackson |
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- name: visibility |
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dtype: |
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class_label: |
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names: |
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'0': best |
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'1': good |
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'2': bad |
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- name: animal_category |
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dtype: |
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class_label: |
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names: |
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'0': chicken |
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'1': duck |
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'2': rooster |
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splits: |
|
- name: train |
|
num_bytes: 22621129760 |
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num_examples: 677 |
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download_size: 22521029844 |
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dataset_size: 22621129760 |
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- config_name: chicken-re-id-best-visibility |
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features: |
|
- name: crop |
|
dtype: image |
|
- name: identity |
|
dtype: |
|
class_label: |
|
names: |
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'0': Beate |
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'1': Borghild |
|
'2': Eleonore |
|
'3': Mona |
|
'4': Henriette |
|
'5': Margit |
|
'6': Millie |
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'7': Sigrun |
|
'8': Kristina |
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'9': Tina |
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'10': Gretel |
|
'11': Lena |
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'12': Yolkoono |
|
'13': Skimmy |
|
'14': Mavi |
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'15': Mirmir |
|
'16': Nugget |
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'17': Fernanda |
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'18': Isolde |
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'19': Mechthild |
|
'20': Brunhilde |
|
'21': Spiderman |
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'22': Brownie |
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'23': Camy |
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'24': Samy |
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'25': Yin |
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'26': Yuriko |
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'27': Renate |
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'28': Regina |
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'29': Monika |
|
'30': Heidi |
|
'31': Erna |
|
'32': Marina |
|
'33': Kathrin |
|
'34': Isabella |
|
'35': Amalia |
|
'36': Edeltraut |
|
'37': Erdmute |
|
'38': Oktavia |
|
'39': Siglinde |
|
'40': Ulrike |
|
'41': Hermine |
|
'42': Matilda |
|
'43': Chantal |
|
'44': Chayenne |
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'45': Jaqueline |
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'46': Mandy |
|
'47': Henny |
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'48': Shady |
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'49': Shorty |
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splits: |
|
- name: train |
|
num_bytes: 1116940840 |
|
num_examples: 630 |
|
- name: test |
|
num_bytes: 282284300 |
|
num_examples: 163 |
|
download_size: 1396952370 |
|
dataset_size: 1399225140 |
|
- config_name: chicken-re-id-all-visibility |
|
features: |
|
- name: crop |
|
dtype: image |
|
- name: identity |
|
dtype: |
|
class_label: |
|
names: |
|
'0': Beate |
|
'1': Borghild |
|
'2': Eleonore |
|
'3': Mona |
|
'4': Henriette |
|
'5': Margit |
|
'6': Millie |
|
'7': Sigrun |
|
'8': Kristina |
|
'9': Tina |
|
'10': Gretel |
|
'11': Lena |
|
'12': Yolkoono |
|
'13': Skimmy |
|
'14': Mavi |
|
'15': Mirmir |
|
'16': Nugget |
|
'17': Fernanda |
|
'18': Isolde |
|
'19': Mechthild |
|
'20': Brunhilde |
|
'21': Spiderman |
|
'22': Brownie |
|
'23': Camy |
|
'24': Samy |
|
'25': Yin |
|
'26': Yuriko |
|
'27': Renate |
|
'28': Regina |
|
'29': Monika |
|
'30': Heidi |
|
'31': Erna |
|
'32': Marina |
|
'33': Kathrin |
|
'34': Isabella |
|
'35': Amalia |
|
'36': Edeltraut |
|
'37': Erdmute |
|
'38': Oktavia |
|
'39': Siglinde |
|
'40': Ulrike |
|
'41': Hermine |
|
'42': Matilda |
|
'43': Chantal |
|
'44': Chayenne |
|
'45': Jaqueline |
|
'46': Mandy |
|
'47': Henny |
|
'48': Shady |
|
'49': Shorty |
|
splits: |
|
- name: train |
|
num_bytes: 1440206054 |
|
num_examples: 916 |
|
- name: test |
|
num_bytes: 356817885 |
|
num_examples: 230 |
|
download_size: 1794292959 |
|
dataset_size: 1797023939 |
|
configs: |
|
- config_name: full-dataset |
|
data_files: |
|
- split: train |
|
path: full-dataset/train-* |
|
- config_name: semantic-segmentation |
|
data_files: |
|
- split: train |
|
path: semantic-segmentation/train-* |
|
- config_name: instance-segmentation |
|
data_files: |
|
- split: train |
|
path: instance-segmentation/train-* |
|
- config_name: animal-category-anomalies |
|
data_files: |
|
- split: train |
|
path: animal-category-anomalies/train-* |
|
- config_name: chicken-re-id-best-visibility |
|
default: true |
|
data_files: |
|
- split: train |
|
path: chicken-re-id-best-visibility/train-* |
|
- split: test |
|
path: chicken-re-id-best-visibility/test-* |
|
- config_name: chicken-re-id-all-visibility |
|
data_files: |
|
- split: train |
|
path: chicken-re-id-all-visibility/train-* |
|
- split: test |
|
path: chicken-re-id-all-visibility/test-* |
|
language: |
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- en |
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tags: |
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- re-identification |
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- chicken |
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- closed-set |
|
- instance segmentation |
|
- semantic segmentation |
|
- poultry |
|
- re-id |
|
- croissant |
|
pretty_name: Chicks4FreeID |
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--- |
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|
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# Dataset Card for Chicks4FreeID |
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<!-- Provide a quick summary of the dataset. --> |
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The very first publicly available dataset for chicken re-identification. |
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## 1 Dataset Details |
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### 1.1 Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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The Chicks4FreeID dataset contains top-down view images of individually segmented and annotated chickens (with roosters and ducks also possibly present and labeled as such). 11 different coops with 54 individuals were visited for manual data collection. Each of the 677 images depicts at least one chicken. The identities of the 50 chickens, 2 roosters and 2 ducks were annotated for a total of 1270 animal instances. Annotation additionally contains visibility ratings of "best", "good", and "bad" for each animal instance. Besides chicken re-identification ,the curated dataset also support semantic and instance segmentation. Corresponding masks for these tasks are provided. |
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> [!IMPORTANT] |
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> For a detailed description and documentation please read the paper and the supplementary material. |
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- **Curated by:** Daria Kern and Tobias Schiele |
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<!--- **Funded by [optional]:** [More Information Needed]--> |
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<!---- **Shared by [optional]:** [More Information Needed]--> |
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- **Language(s) (NLP):** English |
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- **License:** CC-BY-4.0 |
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### 1.2 Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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**Code:** [GitHub](https://github.com/DariaKern/Chicks4FreeID) |
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**Paper:** [Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification](https://doi.org/10.3390/ani15010001) |
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**Supplementary material:** coming soon... |
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**DOI:** [https://doi.org/10.57967/hf/2345](https://doi.org/10.57967/hf/2345) |
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<!--- **Repository:** https://github.com/DariaKern/Chicks4FreeID --> |
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<!--- **Paper [optional]:** [More Information Needed]--> |
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<!---- **Demo [optional]:** [More Information Needed]--> |
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## 2 Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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### 2.1 Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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### 2.2 Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
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Do not use for re-identification of roosters or ducks. |
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## 3 Dataset Structure |
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure--> |
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<!-- such as criteria used to create the splits, relationships between data points, etc. --> |
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Modalities: |
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* 677 images |
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* 1270 preprocessed cut-out crops |
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* 1270 binary instance segmentation masks |
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* 677 color-Coded semantic segmentation masks |
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(classes: chicken, rooster, duck, background) |
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Annotations: |
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* Animal category (chicken, rooster, duck) |
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* Identity (54 unique names) |
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* Coop (1-11) |
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* Visibility (best, good, bad) |
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## 4 Dataset Creation |
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### 4.1 Curation Rationale |
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<!-- Motivation for the creation of this dataset. --> |
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The Chicks4FreeID dataset was created specifically for the task of chicken re-identification - i.e., recognizing the identity of an individual chicken in an image. There were two primary motivations for developing this dataset. First, there is a significant need for publicly available and well-annotated datasets in the field of animal re-identification. Second, there was a notable gap, as no such dataset existed for chickens prior to this effort. |
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However, the dataset is multipurpose and can also be used for semantic segmentation, instance segmentation, or even anomaly detection. It was structured, annotated, and prepared to support these additional tasks effectively. |
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### 4.2 Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### 4.2.1 Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, --> |
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<!-- filtering and normalization methods, tools and libraries used, etc. --> |
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Data was collected manually using two models of cameras: the “Sony CyberShot DSC-RX100 VI”130 and the “Sony CyberShot DSC-RX100 I". The identities of the subjects were meticulously studied prior to photography, closely monitored throughout the image capture process, and ultimately assigned by a human annotator. No algorithms were used. During photography, the focus was always on a single chicken (the chickens were photographed sequentially, not randomly), while other individuals were able to enter the frame as well. The data collection took approximately one year. However, all images of a coop where always taken within a single day. In other words, all photos of an individual were taken on the same day. |
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#### 4.2.2 Who are the source data producers? |
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<!-- This section describes the people or systems who originally created the data. |
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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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Daria Kern collected the data. |
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<!-- ### Annotations [optional]--> |
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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#### 4.2.3 Annotation process |
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<!-- This section describes the annotation process such as annotation tools used in the process, |
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the amount of data annotated, annotation guidelines provided to the annotators, |
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interannotator statistics, annotation validation, etc. --> |
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We utilized Labelbox under a free educational license for manual data annotation. |
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#### 4.2.4 Who are the annotators? |
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<!-- This section describes the people or systems who created the annotations. --> |
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Daria Kern annotated the data. |
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#### 4.2.5 Personal and Sensitive Information |
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<!-- State whether the dataset contains data that might be considered personal, |
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sensitive, or private (e.g., data that reveals addresses, |
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uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, |
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religious beliefs, political opinions, financial or health data, etc.). |
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If efforts were made to anonymize the data, describe the anonymization process. --> |
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The dataset does not contain any personal or sensitive information. It contains images of free-range chickens. |
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## 5 Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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* Changes in appearance over time were not captured as all images of a given chicken were taken on the same day |
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* Despite the variability, chicken breeds included in the dataset are not exhaustive |
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* Be aware of class imbalances: the number of instances ranges from 4 to 27 in the "best" visibility subset |
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<!-- ### 5.1 Recommendations--> |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.--> |
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## 6 Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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```tex |
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@Article{ani15010001, |
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AUTHOR = {Kern, Daria and Schiele, Tobias and Klauck, Ulrich and Ingabire, Winfred}, |
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TITLE = {Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification}, |
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JOURNAL = {Animals}, |
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VOLUME = {15}, |
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YEAR = {2025}, |
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NUMBER = {1}, |
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ARTICLE-NUMBER = {1}, |
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URL = {https://www.mdpi.com/2076-2615/15/1/1}, |
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ISSN = {2076-2615}, |
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DOI = {10.3390/ani15010001} |
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} |
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``` |
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<!-- **APA:** --> |
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<!-- ## 7 Glossary --> |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> |
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<!--## More Information [optional] --> |
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<!-- ## Dataset Card Authors [optional] --> |
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## 7 Dataset Card Contact |
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[email protected] |
