| --- |
| language: |
| - en |
| tags: |
| - vision |
| - multimodal |
| - visual-language-model |
| - image-understanding |
| - vlm |
| - parquet |
| pretty_name: Cleaned VLM Training Dataset |
| size_categories: |
| - 10B<n<100B |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
|
|
|  |
|
|
| # Cleaned VLM Training Dataset |
|
|
| **A filtered visual-language dataset for training models to understand images and interpret visual information.** |
|
|
| `CrowdMind/cleaned-vlm` is a cleaned version of [`CrowdMind/categories`](https://huggingface.co/datasets/CrowdMind/categories). |
|
|
| The dataset focuses on examples with greater visual complexity. Entries labeled `low` in the `visual_complexity` field have been removed, leaving only `medium` and `high` complexity examples. |
|
|
| The original columns are preserved. |
|
|
| ## Dataset Overview |
|
|
| | Property | Description | |
| |---|---| |
| | Dataset | CrowdMind/cleaned-vlm | |
| | Source | CrowdMind/categories | |
| | Format | Parquet | |
| | Modality | Vision and text | |
| | Complexity levels | Medium, High | |
| | Removed entries | Low visual complexity | |
| | Splits | Train | |
| | Storage size | Approximately 22.1 GB | |
| | Shards | 56 | |
|
|
|  |
|
|
| ## What Is This Dataset For? |
|
|
| The goal is to provide a more focused collection of visual-language examples for training and experimentation. |
|
|
| Potential applications include: |
|
|
| - **Visual understanding:** Learning to interpret image content. |
| - **Visual question answering:** Answering questions using visual evidence. |
| - **Image reasoning:** Understanding relationships between objects and visual elements. |
| - **Scene interpretation:** Recognizing and describing complex visual situations. |
| - **Multimodal instruction tuning:** Connecting natural-language instructions with visual inputs. |
| - **VLM pretraining and fine-tuning:** Improving visual-language capabilities in multimodal models. |
|
|
| ## Data Processing |
|
|
| This dataset was derived from `CrowdMind/categories`. |
|
|
| The cleaning process: |
|
|
| 1. Load the original dataset. |
| 2. Inspect the `visual_complexity` field. |
| 3. Remove entries labeled `low`. |
| 4. Retain entries labeled `medium` or `high`. |
| 5. Preserve the existing dataset columns. |
| 6. Export the resulting dataset into Parquet shards. |
|
|
| No additional labels or annotations are claimed to have been generated during this filtering process. |
|
|
| ## Visual Complexity |
|
|
| The dataset retains two complexity levels: |
|
|
| | Label | Included | Description | |
| |---|---|---| |
| | Low | No | Removed during filtering | |
| | Medium | Yes | Retained from the source dataset | |
| | High | Yes | Retained from the source dataset | |
|
|
| The filtering is intended to create a more focused training collection containing examples with greater visual complexity. |
|
|
| ## Dataset Structure |
|
|
| The repository stores its training data in Parquet format. |
|
|
| ```text |
| cleaned-vlm/ |
| ├── README.md |
| └── data/ |
| ├── train-00000-of-00056.parquet |
| ├── train-00001-of-00056.parquet |
| ├── train-00002-of-00056.parquet |
| ├── ... |
| └── train-00055-of-00056.parquet |
| ``` |
|
|
| The dataset preserves the original columns from `CrowdMind/categories`. |
|
|
| ## Intended Use |
|
|
| This dataset is intended for research and development involving: |
|
|
| - Vision-language models (VLMs). |
| - Multimodal foundation models. |
| - Visual reasoning systems. |
| - Image understanding and interpretation. |
| - Multimodal supervised fine-tuning. |
| - Experiments involving visual complexity and model performance. |
|
|
| Researchers can use this collection to investigate whether training on more visually complex examples improves a model's ability to understand and reason about images. |
|
|
| ## Limitations |
|
|
| - The dataset excludes examples labeled with low visual complexity. |
| - It does not represent the full distribution of the original source dataset. |
| - Filtering may introduce a bias toward more visually complex examples. |
| - The dataset's usefulness depends on the quality and diversity of its original annotations. |
| - Performance improvements from this filtering approach have not been established by the dataset itself. |
|
|
| ## Source Dataset |
|
|
| The original dataset: |
|
|
| [CrowdMind/categories](https://huggingface.co/datasets/CrowdMind/categories) |
|
|
| ## Maintainer |
|
|
| **CrowdMind** |
|
|
| Hugging Face: https://huggingface.co/CrowdMind |
