---
license: other
language:
- en
pretty_name: bluelens
size_categories:
- n>1T
---
<div align="center">
  <img src="docs/images/bluelens.png" alt="BlueLens Logo" width="300"/>
</div>

# Dataset Card for BlueLense

## Dataset Summary
<!-- Provide a detailed description of the dataset and it purpose. -->

## Dataset Details


| Model     | Description                         | Checkpoint Used         | Dataset | Split       |
|:----------|:------------------------------------|:-------------------------|:--------|:------------|
| GDINO     | Features from COCO 2017 training set | [`groundingdino-swint-ogc`](https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth) | COCO    | COCO_TRAIN  |
| GDINO     | Features from COCO 2017 validation set | [`groundingdino-swint-ogc`](https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth) | COCO    | COCO_VAL    |
| GDINO     | ~100 random samples from COCO 2017   | [`groundingdino-swint-ogc`](https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth) | COCO    | COCO_MINI   |
| DETR      | ~100 random samples from COCO 2017          | [`detr_r50_8xb2-150e_coco_20221023_153551-436d03e8`](https://download.openmmlab.com/mmdetection/v3.0/detr/detr_r50_8xb2-150e_coco/detr_r50_8xb2-150e_coco_20221023_153551-436d03e8.pth)                      | COCO    | COCO_MINI  |
| DINO-DETR | ~100 random samples from COCO 2017          | [`dino-5scale_swin-l_8xb2-36e_coco`](https://github.com/RistoranteRist/mmlab-weights/releases/download/dino-swinl/dino-5scale_swin-l_8xb2-36e_coco-5486e051.pth)                      | COCO    | COCO_MINI  |



- **Creators**: Intel Labs
- **Version**: 1.0 (Updated: 2025-05-02)
- **License**: [Intel Research and Development License](Intel_OBL_Internal_RD_Use_License.md)
- **Number of Training Samples**: >100M
- **Number of Test Samples**: >500K

- **Format**: pyarrow format

## Intended Use

- **Primary Uses**:
  - These intermediate features or tokens are primarily intended for insights and exploratory analysis in explainable AI, as well as for training ad-hoc models such as linear probes, Sparse AutoEncoders (SAEs), or transcoders.
  - This is also an example of a dataset that can be extracted using the feature recorder (intercept_manager) from the BlueGlass repository, providing a guiding dataset for further analysis.
- **Out-of-Scope Uses**: This dataset is not intended for commercial use or for training models that will be deployed in real-world scenarios without further verification and validation.

## Data Collection Process

This dataset contains intermediate features extracted from various layers of transformer models using the intercept_manager module from [BlueGlass](https://github.com/IntelLabs/blueglass). The features are recorded from different probe positions within the model, as illustrated in the image below, enabling fine-grained analysis and interoperability.

<div align="center">
  <img src="docs/images/feature_pattern.png" alt="Feature Pattern used to extract the BlueLens dataset" width="300"/>
</div>


## Ethical Considerations
<!-- DON'T CHANGE THIS SECTION -->
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

## Contact Information

- **Issues**: For any issues or questions regarding the dataset, please contact the maintainers or open an issue in the dataset repository.