| | --- |
| | license: other |
| | license_name: intel-research-development-license |
| | license_link: LICENSE.md |
| | language: |
| | - en |
| | pretty_name: bluelens |
| | size_categories: |
| | - n>1T |
| | --- |
| | |
| | # Dataset Card for BlueLense |
| | <div align="center"> |
| | <img src="docs/images/bluelens.png" alt="BlueLens Logo" width="300"/> |
| | </div> |
| |
|
| | ## 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. |
