akankshanc/inception-v1-microscope-data

Inception V1 Microscope Data

This dataset powers the Inception V1 Microscope, an interactive interface for exploring visual features learned by individual neurons in Inception V1.

It combines two complementary interpretability views:

1. 1.Activation maximization: one synthesized visualization optimized to strongly activate each neuron.
2. 2.Top dataset examples: the ten ImageNet examples producing the strongest recorded activations for each neuron, paired with crops associated with the activating regions.

These visualizations are evidence about model behavior, not definitive semantic labels for neurons.

Dataset scale

The production release covers 5,804 neurons across 12 layers.

Each ranked pair contains two images, giving 116,080 natural-image and crop records, in addition to the 5,804 synthesized activation visualizations.

Configurations

activation_maximization

This configuration contains one row per neuron.

The configuration uses the split name train as a Hugging Face storage label. These records are visualizations and are not a model-training set.

dataset_examples

This configuration uses one split per layer and contains ten rows per neuron. Each row keeps the full source image and its crop together.

Example

from datasets import load_dataset

activation = load_dataset(
    "akankshanc/inception-v1-microscope-data",
    "activation_maximization",
    split="train",
)

mixed4a_examples = load_dataset(
    "akankshanc/inception-v1-microscope-data",
    "dataset_examples",
    split="mixed4a",
)

neuron = mixed4a_examples.filter(
    lambda row: row["channel"] == 254
).sort("rank")

print(neuron)

Methodology

Activation maximization

Each synthesized image was produced by optimizing a parameterized input to increase the response of one selected Inception V1 channel. The resulting image provides a visual hypothesis about patterns that strongly excite that neuron under the chosen optimization procedure.

Dataset examples

Natural images were scored for each neuron. The ten highest-scoring examples were retained and ordered by raw activation score. Each full image was paired with a precomputed crop associated with its strongly activating region.

Validation

The release was checked for:

• —5,804 activation-maximization rows;
• —exactly ten ranked example pairs per neuron;
• —expected row counts for all 12 layer splits;
• —matching model, layer, channel, and neuron identifiers;
• —complete rank sets from 1 through 10;
• —presence of both FULL and CROP image fields;
• —correct first and last records at every layer boundary; and
• —image URL availability through the Hugging Face Dataset Viewer API.

Intended use

This dataset is intended for non-commercial research and educational work on:

• —neural-network interpretability;
• —feature visualization;
• —qualitative analysis of convolutional representations;
• —interpretability interfaces and teaching demonstrations; and
• —comparisons between synthesized features and natural-image evidence.

Limitations

• —A visualization is an interpretability aid, not a definitive neuron label or complete causal explanation.
• —Activation-maximization results depend on the checkpoint, objective, parameterization, regularization, and optimization procedure.
• —Top examples characterize the evaluated image collection and may not cover every pattern that activates a neuron.
• —Crops can omit contextual information that contributes to the full-image response.
• —Raw activation scores are suitable for ranking examples within a neuron but should not be compared directly across layers or channels.
• —The natural-image examples inherit biases and coverage limitations from ImageNet.

Data provenance and terms

The natural-image examples are derived from ImageNet and are provided for non-commercial research and educational interpretability work. ImageNet does not own the copyright in the underlying images; individual images may remain subject to their original copyright and applicable ImageNet access terms. This repository does not relicense those source images.

Review the ImageNet terms of access before downloading, redistributing, or repurposing the natural-image examples.

References

• —Szegedy, C. et al. Going Deeper with Convolutions. CVPR 2015. Paper
• —Deng, J. et al. ImageNet: A Large-Scale Hierarchical Image Database. CVPR 2009. ImageNet
• —Olah, C. et al. The Building Blocks of Interpretability. Distill, 2018. Article
• —OpenAI. OpenAI Microscope. Project

Citation

@misc{devkar_inception_v1_microscope_data_2026,
  author       = {Akanksha Devkar},
  title        = {Inception V1 Microscope Data},
  year         = {2026},
  howpublished = {Hugging Face Dataset},
  url          = {https://huggingface.co/datasets/akankshanc/inception-v1-microscope-data}
}