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