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--- |
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license: apache-2.0 |
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task_categories: |
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- video-text-to-text |
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- image-to-text |
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language: |
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- en |
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tags: |
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- colab |
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- notebook |
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- demo |
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- vlm |
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- models |
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- hf |
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- ocr |
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- reasoning |
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- code |
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size_categories: |
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- n<1K |
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--- |
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# **VLM-Video-Understanding** |
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> A minimalistic demo for image inference and video understanding using OpenCV, built on top of several popular open-source Vision-Language Models (VLMs). This repository provides Colab notebooks demonstrating how to apply these VLMs to video and image tasks using Python and Gradio. |
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## Overview |
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This project showcases lightweight inference pipelines for the following: |
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- Video frame extraction and preprocessing |
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- Image-level inference with VLMs |
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- Real-time or pre-recorded video understanding |
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- OCR-based text extraction from video frames |
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## Models Included |
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The repository supports a variety of open-source models and configurations, including: |
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- Aya-Vision-8B |
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- Florence-2-Base |
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- Gemma3-VL |
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- MiMo-VL-7B-RL |
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- MiMo-VL-7B-SFT |
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- Qwen2-VL |
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- Qwen2.5-VL |
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- Qwen-2VL-MessyOCR |
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- RolmOCR-Qwen2.5-VL |
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- olmOCR-Qwen2-VL |
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- typhoon-ocr-7b-Qwen2.5VL |
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Each model has a dedicated Colab notebook to help users understand how to use it with video inputs. |
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## Technologies Used |
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- **Python** |
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- **OpenCV** – for video and image processing |
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- **Gradio** – for interactive UI |
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- **Jupyter Notebooks** – for easy experimentation |
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- **Hugging Face Transformers** – for loading VLMs |
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## Folder Structure |
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``` |
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├── Aya-Vision-8B/ |
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├── Florence-2-Base/ |
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├── Gemma3-VL/ |
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├── MiMo-VL-7B-RL/ |
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├── MiMo-VL-7B-SFT/ |
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├── Qwen2-VL/ |
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├── Qwen2.5-VL/ |
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├── Qwen-2VL-MessyOCR/ |
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├── RolmOCR-Qwen2.5-VL/ |
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├── olmOCR-Qwen2-VL/ |
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├── typhoon-ocr-7b-Qwen2.5VL/ |
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├── LICENSE |
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└── README.md |
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```` |
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## Getting Started |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/PRITHIVSAKTHIUR/VLM-Video-Understanding.git |
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cd VLM-Video-Understanding |
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```` |
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2. Open any of the Colab notebooks and follow the instructions to run image or video inference. |
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3. Optionally, install dependencies locally: |
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```bash |
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pip install opencv-python gradio transformers |
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``` |
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## Hugging Face Dataset |
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The models and examples are supported by a dataset on Hugging Face: |
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[VLM-Video-Understanding](https://huggingface.co/datasets/prithivMLmods/VLM-Video-Understanding) |
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## License |
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This project is licensed under the Apache-2.0 License. |
