class

InceptionResNetV2ForImageClassification

extendsPretrainedModel

InceptionResNetV2ForImageClassification(config: InceptionResNetConfig)

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Inception-ResNet v2 image classifier (backbone + GAP + dropout + linear).

Combines an InceptionResNetV2 backbone with a global-average-pool, optional lucid.nn.Dropout (p=config.dropout, default 0.2), and a single lucid.nn.Linear projection producing config.num_classes logits. No auxiliary classifier — the residual shortcuts already provide effective gradient flow throughout the network. The classifier attribute is named classif (not classifier) to match the canonical timm / TensorFlow-Slim state-dict key.

Parameters

configInceptionResNetConfig

Architecture spec. Use inception_resnet_v2_cls for the paper-cited configuration.

Attributes

configInceptionResNetConfig

Stored copy of the config that built this model.

conv2d_1a … conv2d_7b, mixed_5b, repeat, mixed_6a, repeat_1,

mixed_7a, repeat_2, block8 Same backbone components as on InceptionResNetV2.

avgpoolnn.AdaptiveAvgPool2d

Final global average pool to

1\times1

dropoutnn.Module

lucid.nn.Dropout when config.dropout > 0 (default 0.2), otherwise lucid.nn.Identity.

classifnn.Linear

Final classifier projecting 1536 → num_classes — named classif for state-dict compatibility.

Notes

From Szegedy et al., "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", AAAI 2017. Loss is the standard categorical cross-entropy

\mathcal{L} = -\frac{1}{N} \sum_{n=1}^{N} \log \operatorname{softmax}(\text{logits}_n)_{\,y_n},

computed only when labels is not None. Approximately 55.8 M parameters; top-5 ImageNet validation error 3.08%. The key empirical message of the paper is that residual connections do not raise the final accuracy ceiling beyond a comparably-sized non-residual Inception v4, but they dramatically accelerate training convergence.

Examples

>>> import lucid
>>> from lucid.models.vision.inception_resnet import inception_resnet_v2_cls
>>> model = inception_resnet_v2_cls()
>>> x = lucid.randn(2, 3, 299, 299)
>>> out = model(x)
>>> out.logits.shape
(2, 1000)

Used by 2

Constructors

dunder

init

→None

__init__(config: InceptionResNetConfig)

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Instance methods

forward

→InceptionResNetOutput

forward(x: Tensor, labels: Tensor | None = None)

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>>> import lucid >>> from lucid.models.vision.inception_resnet import inception_resnet_v2_cls >>> model = inception_resnet_v2_cls() >>> x = lucid.randn(2, 3, 299, 299) >>> out = model(x) >>> out.logits.shape (2, 1000)