---
pretty_name: Inception V1 Microscope Data
tags:
  - computer-vision
  - interpretability
  - feature-visualization
dataset_info:
  - config_name: activation_maximization
    features:
      - name: model
        dtype: string
      - name: layer
        dtype: string
      - name: channel
        dtype: int32
      - name: neuron_id
        dtype: string
      - name: original_filename
        dtype: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 1408434214
        num_examples: 5804
    download_size: 2518344881
    dataset_size: 1408434214
  - config_name: dataset_examples
    features:
      - name: model
        dtype: string
      - name: layer
        dtype: string
      - name: channel
        dtype: int32
      - name: neuron_id
        dtype: string
      - name: rank
        dtype: int16
      - name: activation_score
        dtype: float32
      - name: source_image_id
        dtype: string
      - name: full_image
        dtype: image
      - name: crop_image
        dtype: image
    splits:
      - name: conv2d0
        num_bytes: 8843639
        num_examples: 640
      - name: conv2d1
        num_bytes: 8867534
        num_examples: 640
      - name: conv2d2
        num_bytes: 25732765
        num_examples: 1920
      - name: mixed3a
        num_bytes: 34633461
        num_examples: 2560
      - name: mixed3b
        num_bytes: 63885612
        num_examples: 4800
      - name: mixed4a
        num_bytes: 64855685
        num_examples: 5080
      - name: mixed4b
        num_bytes: 63178857
        num_examples: 5120
      - name: mixed4c
        num_bytes: 63797176
        num_examples: 5120
      - name: mixed4d
        num_bytes: 63804127
        num_examples: 5280
      - name: mixed4e
        num_bytes: 97750616
        num_examples: 8320
      - name: mixed5a
        num_bytes: 99572465
        num_examples: 8320
      - name: mixed5b
        num_bytes: 122531881
        num_examples: 10240
    download_size: 726255360
    dataset_size: 717453818
configs:
  - config_name: activation_maximization
    data_files:
      - split: train
        path: activation_maximization/train-*
  - config_name: dataset_examples
    data_files:
      - split: conv2d0
        path: dataset_examples/conv2d0-*
      - split: conv2d1
        path: dataset_examples/conv2d1-*
      - split: conv2d2
        path: dataset_examples/conv2d2-*
      - split: mixed3a
        path: dataset_examples/mixed3a-*
      - split: mixed3b
        path: dataset_examples/mixed3b-*
      - split: mixed4a
        path: dataset_examples/mixed4a-*
      - split: mixed4b
        path: dataset_examples/mixed4b-*
      - split: mixed4c
        path: dataset_examples/mixed4c-*
      - split: mixed4d
        path: dataset_examples/mixed4d-*
      - split: mixed4e
        path: dataset_examples/mixed4e-*
      - split: mixed5a
        path: dataset_examples/mixed5a-*
      - split: mixed5b
        path: dataset_examples/mixed5b-*
---

# Inception V1 Microscope Data

This dataset powers the [Inception V1
Microscope](https://huggingface.co/spaces/akankshanc/inception-v1-microscope),
an interactive interface for exploring visual features learned by individual
neurons in Inception V1.

It combines two complementary interpretability views:

1. **Activation maximization:** one synthesized visualization optimized to
   strongly activate each neuron.
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**.

| Layer | Channels | Activation visualizations | Ranked FULL/CROP pairs |
| --- | ---: | ---: | ---: |
| `conv2d0` | 64 | 64 | 640 |
| `conv2d1` | 64 | 64 | 640 |
| `conv2d2` | 192 | 192 | 1,920 |
| `mixed3a` | 256 | 256 | 2,560 |
| `mixed3b` | 480 | 480 | 4,800 |
| `mixed4a` | 508 | 508 | 5,080 |
| `mixed4b` | 512 | 512 | 5,120 |
| `mixed4c` | 512 | 512 | 5,120 |
| `mixed4d` | 528 | 528 | 5,280 |
| `mixed4e` | 832 | 832 | 8,320 |
| `mixed5a` | 832 | 832 | 8,320 |
| `mixed5b` | 1,024 | 1,024 | 10,240 |
| **Total** | **5,804** | **5,804** | **58,040** |

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.

| Field | Type | Description |
| --- | --- | --- |
| `model` | string | Model identifier (`inception_v1`) |
| `layer` | string | Selected layer |
| `channel` | int32 | Zero-indexed channel number |
| `neuron_id` | string | Stable layer/channel identifier |
| `original_filename` | string | Source visualization filename |
| `image` | image | Activation-maximization visualization |

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.

| Field | Type | Description |
| --- | --- | --- |
| `model` | string | Model identifier (`inception_v1`) |
| `layer` | string | Selected layer |
| `channel` | int32 | Zero-indexed channel number |
| `neuron_id` | string | Stable layer/channel identifier |
| `rank` | int16 | Rank from 1 through 10 within the neuron |
| `activation_score` | float32 | Raw response used for within-neuron ranking |
| `source_image_id` | string | ImageNet source identifier |
| `full_image` | image | Full natural image |
| `crop_image` | image | Crop associated with the activating region |

## Example

```python
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](https://www.image-net.org/download.php) before downloading,
redistributing, or repurposing the natural-image examples.

## References

- Szegedy, C. et al. **Going Deeper with Convolutions.** CVPR 2015.
  [Paper](https://arxiv.org/abs/1409.4842)
- Deng, J. et al. **ImageNet: A Large-Scale Hierarchical Image Database.**
  CVPR 2009. [ImageNet](https://www.image-net.org/)
- Olah, C. et al. **The Building Blocks of Interpretability.** Distill, 2018.
  [Article](https://distill.pub/2018/building-blocks/)
- OpenAI. **OpenAI Microscope.**
  [Project](https://microscope.openai.com/)

## Citation

```bibtex
@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}
}
```