| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: image_source |
| dtype: string |
| - name: response |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 4004176789 |
| num_examples: 158958 |
| download_size: 3837976031 |
| dataset_size: 4004176789 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| tags: |
| - agent-traces |
| - image-captioning |
| - dense-captioning |
| - uhd |
| - computer-vision |
| - multimodal |
| - synthetic-captions |
| - deep-caption |
| - image |
| license: apache-2.0 |
| task_categories: |
| - image-text-to-text |
| - image-to-text |
| language: |
| - en |
| pretty_name: MultiCaptions |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # **OpenCOCO-I2T-Repack-Tiny** |
|
|
| > **OpenCOCO-I2T-Repack-Tiny** is a compact **image-to-text / image-text-to-text captioning dataset** containing **158,958 image samples** sourced from the **COCO dataset** and repackaged into a lightweight format suitable for vision-language model (VLM) fine-tuning. The dataset contains synthesized responses generated using a **custom Qwen3.5 multimodal captioning pipeline**. The input images undergo **lossless image compression** to significantly reduce the overall storage footprint while preserving the visual information required for training and inference. With approximately **159K samples compressed into only 3.84 GB**, OpenCOCO-I2T-Repack-Tiny is designed to provide an easily accessible and efficient dataset for VLM-based fine-tuning, experimentation, and image understanding tasks. |
|
|
| ## Dataset Overview |
|
|
| | Property | Details | |
| | ------------------ | ------------------------------------- | |
| | Dataset Name | **OpenCOCO-I2T-Repack-Tiny** | |
| | Samples | **158,958** | |
| | Task | Image-to-Text / Image-Text-to-Text | |
| | Modality | Image + Text | |
| | Image Source | COCO | |
| | Caption Generation | Qwen3.5 Custom Multimodal Pipeline | |
| | Format | Parquet | |
| | Dataset Size | **3.84 GB** | |
| | License | Apache-2.0 | |
| | Primary Use | VLM Fine-Tuning / Image Understanding | |
|
|
| ## Description |
|
|
| OpenCOCO-I2T-Repack-Tiny is a repackaged version of COCO-based image data designed specifically for efficient multimodal training workflows. |
|
|
| The dataset consists of: |
|
|
| * **158,958 image-text samples** |
| * Images sourced from the publicly available **COCO dataset** |
| * Rich textual responses generated through a custom **Qwen3.5 multimodal pipeline** |
| * Losslessly compressed input images |
| * A compact Parquet-based dataset structure |
| * Image source metadata preserved alongside the generated responses |
|
|
| The goal is to provide a relatively small storage footprint while retaining a large number of image-text training examples. |
|
|
| ## Dataset Construction |
|
|
| The dataset construction pipeline consists of several stages: |
|
|
| 1. **Image Collection** |
|
|
| * Images are sourced from the COCO dataset. |
| * The original image source is retained through the `image_source` field. |
|
|
| 2. **Multimodal Caption Generation** |
|
|
| * Images are processed through a custom Qwen3.5-based multimodal pipeline. |
| * The pipeline generates descriptive textual responses based on the visual content of each image. |
|
|
| 3. **Image Compression** |
|
|
| * Input images undergo lossless image compression. |
| * The objective is to reduce storage requirements without introducing lossy visual degradation. |
|
|
| 4. **Dataset Repacking** |
|
|
| * Images and generated responses are consolidated into a compact dataset structure. |
| * The final dataset is packaged in Parquet format for efficient loading and processing. |
|
|
| 5. **VLM Training Preparation** |
|
|
| * The resulting image-text pairs can be directly adapted for multimodal fine-tuning workflows. |
|
|
| ## Dataset Structure |
|
|
| The dataset contains the following primary fields: |
|
|
| ```text |
| image |
| image_source |
| response |
| ``` |
|
|
| ### `image` |
|
|
| The compressed input image used for multimodal captioning and analysis. |
|
|
| ### `image_source` |
| |
| The source associated with the original image. |
| |
| For COCO samples, the source is: |
| |
| [COCO Dataset](https://cocodataset.org/) |
| |
| ### `response` |
| |
| The synthesized textual response generated by the custom Qwen3.5 multimodal pipeline. |
| |
| The responses are intended to provide rich visual descriptions suitable for image understanding and VLM training. |
| |
| ## Example |
| |
| ```text |
| image: |
| [COCO image] |
| |
| image_source: |
| http://cocodataset.org/ |
|
|
| response: |
| This outdoor wooden table scene features a hearty breakfast setup, bathed in bright sunlight. In the foreground, multiple plates and food items are arranged across the table... |
| ``` |
| |
| ## Dataset Statistics |
| |
| * **Total Samples:** 158,958 |
| * **Approximate Dataset Size:** 3.84 GB |
| * **Source:** COCO |
| * **Task:** Image-to-Text / Image-Text-to-Text |
| * **Image Compression:** Lossless |
| * **Caption Generation:** Qwen3.5 Custom Multimodal Pipeline |
| |
| The compact storage footprint makes the dataset convenient for local experimentation, cloud training, and VLM fine-tuning environments with limited storage capacity. |
| |
| ## Intended Use |
| |
| OpenCOCO-I2T-Repack-Tiny can be used for: |
| |
| * Vision-Language Model fine-tuning |
| * Image captioning |
| * Image understanding |
| * Visual instruction tuning |
| * Multimodal representation learning |
| * Image-to-text generation |
| * Image-text alignment |
| * VLM benchmarking and experimentation |
| * Lightweight multimodal training pipelines |
| |
| ## Why OpenCOCO-I2T-Repack-Tiny? |
| |
| The primary goal of this repack is **efficiency**. |
| |
| Rather than maintaining a large raw image collection, the dataset combines a high sample count with aggressive **lossless compression** and an efficient Parquet-based representation. |
| |
| This results in: |
| |
| > **158,958 image-text samples in approximately 3.84 GB.** |
| |
| This makes the dataset particularly useful for researchers and developers who want a large number of multimodal examples without requiring an excessively large amount of local or cloud storage. |
| |
| ## Data Source |
| |
| The underlying images are sourced from the **COCO dataset**. |
| |
| [COCO Dataset](https://cocodataset.org/) |
| |
| This dataset is a repackaged and processed resource containing generated textual responses and compressed image representations. |
| |
| Users should review the original source dataset's terms and attribution requirements when using the data. |
| |
| ## Citation |
| |
| If you use **OpenCOCO-I2T-Repack-Tiny** in your research or project, please cite: |
| |
| ```bibtex |
| @misc{prithiv_sakthi_2026, |
| author = {Prithiv Sakthi}, |
| title = {OpenCOCO-I2T-Repack-Tiny (Revision a7ae40c)}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/prithivMLmods/OpenCOCO-I2T-Repack-Tiny}, |
| doi = {10.57967/hf/10057}, |
| publisher = {Hugging Face} |
| } |
| ``` |
| |
| Dataset page: |
|
|
| [OpenCOCO-I2T-Repack-Tiny on Hugging Face](https://huggingface.co/datasets/prithivMLmods/OpenCOCO-I2T-Repack-Tiny) |
|
|
| Dataset creator: |
|
|
| [prithivMLmods on Hugging Face](https://huggingface.co/prithivMLmods) |
|
|
| ## License |
|
|
| This dataset is released under the **Apache-2.0** license as indicated by the dataset repository. |
|
|
| Because the underlying imagery originates from COCO, users should also review the applicable terms, attribution requirements, and usage conditions associated with the original source material. |
