Datasets:
image imagewidth (px) 32 32 | concepts list | label class label 2
classes | label_name stringclasses 2
values |
|---|---|---|---|
[
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] | 0drent | drent | |
[
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] | 0drent | drent | |
[
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] | 0drent | drent | |
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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] | 0drent | drent | |
[
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1
] | 1glorp | glorp | |
[
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1,
0,
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] | 0drent | drent | |
[
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] | 1glorp | glorp | |
[
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] | 0drent | drent | |
[
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] | 1glorp | glorp | |
[
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] | 0drent | drent | |
[
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[
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[
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[
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[
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[
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[
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] | 0drent | drent | |
[
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] | 0drent | drent | |
[
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] | 1glorp | glorp | |
[
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] | 0drent | drent | |
[
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] | 0drent | drent | |
[
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[
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] | 1glorp | glorp | |
[
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] | 1glorp | glorp | |
[
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] | 1glorp | glorp | |
[
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] | 0drent | drent | |
[
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[
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[
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[
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[
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[
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[
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] | 1glorp | glorp | |
[
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[
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] | 1glorp | glorp | |
[
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[
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[
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] | 1glorp | glorp | |
[
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[
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[
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[
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] | 1glorp | glorp | |
[
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] | 1glorp | glorp | |
[
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[
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] | 1glorp | glorp | |
[
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[
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] | 1glorp | glorp | |
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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] | 1glorp | glorp | |
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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] | 1glorp | glorp | |
[
0,
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1,
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] | 0drent | drent | |
[
0,
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] | 1glorp | glorp | |
[
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] | 0drent | drent | |
[
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[
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End of preview. Expand in Data Studio
Robots — Human Concepts
Synthetic benchmark for evaluating Concept Bottleneck Models (CBMs) with misspecified concepts. Same robots, labels and splits as robots-true-concepts, but foot_shape is replaced by the six foot subtypes that appear in the training split. The test split also holds four subtypes never seen in training, for which every foot concept is 0.
Generated from
This dataset is the exact output of the concept-benchmark Python package, with the train/validation/test split of the paper.
# pip install concept-benchmark==0.4.0
from concept_benchmark.robots import DatasetGenerator
dataset = DatasetGenerator(seed=1014, concept_preset='foot_subtypes').generate_splits()
📓 Quickstart notebook — load from the Hub and train a model, end-to-end in one notebook.
Quick start (datasets library)
from datasets import load_dataset
ds = load_dataset("juliannski/robots-human-concepts")
row = ds["train"][0]
row["image"] # PIL.Image
row["concepts"] # 12 binary values, ordered as concept_names below
row["label"] # 0 or 1
row["label_name"] # "drent" or "glorp"
Schema
| Field | Type | Notes |
|---|---|---|
image |
datasets.Image() |
PNG, embedded |
concepts |
Sequence(int8, length=12) |
binary; index order = concept_names below |
label |
ClassLabel("drent", "glorp") |
|
label_name |
string |
display alias for label |
concept_names (column order in concepts):
['head_shape', 'body_shape', 'has_knees', 'has_antennae', 'ears_shape', 'mouth_type', 'foot_shape_flat_trapezoid', 'foot_shape_flat_square', 'foot_shape_flat_5sided', 'foot_shape_pointy_rounded', 'foot_shape_pointy_square', 'foot_shape_pointy_4sided']
Labeling rule
Labels follow the package's default balanced rule: no single concept decides the label, and both classes are equally likely. has_elbows also carries weight in the rule but is not among the concepts, so it is visible in the image and cannot be intervened on. The training split is skewed toward six foot subtypes (pointy_4sided 30%, flat_5sided 30%, pointy_square 10%, pointy_rounded 10%, flat_square 10%, flat_trapezoid 10%); the test split is drawn from all ten. Pass label_rule="sparse" to the generator for the rule with a rare class.
Splits
| Split | n |
|---|---|
train |
3,800 |
validation |
3,384 |
test |
10,000 |
Seed 1014. 51% of the training examples are glorp.
License
MIT.
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