Datasets:

CrowdMind
/

cleaned-vlm

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.

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:

Load the original dataset.
Inspect the visual_complexity field.
Remove entries labeled low.
Retain entries labeled medium or high.
Preserve the existing dataset columns.
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.

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

Maintainer

CrowdMind

Hugging Face: https://huggingface.co/CrowdMind