metadata
license: apache-2.0
task_categories:
- image-segmentation
- depth-estimation
- image-to-image
- image-feature-extraction
- image-text-to-text
language:
- en
tags:
- vlm
- multimodal
- visual-reasoning
- chain-of-thought
dataset_info:
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configs:
- config_name: part1
data_files:
- split: train
path: part1/train-*
- config_name: part10
data_files:
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path: part10/train-*
- config_name: part11
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path: part11/train-*
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path: part12/train-*
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path: part13/train-*
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path: part14/train-*
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path: part15/train-*
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path: part16/train-*
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path: part17/train-*
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path: part18/train-*
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path: part19/train-*
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path: part2/train-*
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path: part20/train-*
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path: part3/train-*
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path: part4/train-*
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path: part5/train-*
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path: part6/train-*
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path: part7/train-*
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data_files:
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path: part8/train-*
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data_files:
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path: part9/train-*
CoVT Dataset
This repository contains the training data for Chain-of-Visual-Thought (CoVT), a framework proposed in the paper "Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens".
CoVT enables Vision-Language Models (VLMs) to reason not only in words but also through continuous visual tokens—compact latent representations that encode rich perceptual cues. This framework addresses the limitations of current VLMs in capturing dense visual information across spatial dimensions, which is essential for perceptual understanding, spatial reasoning, and geometric awareness.
- Paper: https://huggingface.co/papers/2511.19418
- Project Page: https://wakalsprojectpage.github.io/comt-website
- Code: https://github.com/Wakals/CoMT
Dataset Composition
The CoVT dataset is designed to provide dense supervision signals for training VLMs. It utilizes some subsets of LLaVA-OneVision and merges the filtered TallyQA dataset and ADE20K-Depth from Aurora.
During training, a VLM equipped with CoVT autoregressively predicts these visual tokens to reconstruct dense supervision signals such as depth, segmentation, edges, and DINO features.
Sample Usage
To load a specific part of the CoVT dataset, you can use the Hugging Face datasets library:
from datasets import load_dataset
# Load a specific configuration (e.g., 'part1')
dataset = load_dataset("Wakals/CoVT-Dataset", "part1")
# Access the training split
train_data = dataset["train"]
# Print the first example
print(train_data[0])
# Example of accessing image and conversations from a sample
# first_example = train_data[0]
# image_obj = first_example["image"] # This will be a PIL Image object
# print(f"Image format: {image_obj.format}, size: {image_obj.size}")
# print(f"Conversations: {first_example['conversations']}")