video-understanding
VLM-Video-Understanding
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… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/VLM-Video-Understanding.VLM-Video-Understanding
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… See the full description on the dataset page: https://huggingface.co/datasets/hthhththt/VLM-Video-Understanding.video-understanding-distillation-sample
Video Understanding Distillation Sample
This public sample demonstrates what a training-ready video understanding / multimodal distillation dataset can look like.
Intended purpose
This dataset is not a production corpus. It is a schema demonstration for potential partners evaluating SuperviseLab's delivery approach.
What it shows
clip-level metadata
short and long captions
OCR text
transcript
speaker attribution
structured JSON targets
distillation-ready… See the full description on the dataset page: https://huggingface.co/datasets/metavi/video-understanding-distillation-sample.video-understanding-distillation-sample
Video Understanding Distillation Sample
This public sample shows what a training-ready video understanding distillation dataset can look like.
Why this exists
Most teams evaluating outside data vendors want to know one thing first:
What does the delivered data actually look like?
This sample is designed to answer that question.
It demonstrates how raw video clips can be converted into structured, model-ready supervision for:
video understanding
multimodal SFT… See the full description on the dataset page: https://huggingface.co/datasets/superviselab/video-understanding-distillation-sample.skillsbench-audio-video-understandingvideo_understanding