Satya Mallick
@LearnOpenCV
CEO, http://OpenCV.org. Course Director, http://OpenCV.org/Courses Entrepreneur. Ph.D. ( Computer Vision & Machine Learning ). Author: http://LearnOpenCV.com
Może Ci się spodobać
A math professor noticed his kitchen sink at home was leaking. He called a plumber. The plumber came the next day, tightened a couple of nuts, and the sink worked perfectly again. The professor was delighted. But when, a minute later, the plumber handed him the bill, he was…
📢Revolutionizing OCR: How DeepSeek-OCR Solves Token Explosion A picture is worth a thousand words, but processing those words shouldn't cost a fortune in compute! Enter DeepSeek OCR: A game-changing VLM that compresses complex document visuals into just 64 - 400 tokens. What’s…
Recently, there was a clash between the popular @FFmpeg project, a low-level multimedia library found everywhere… and Google. A Google AI agent found a bug in FFmpeg. FFmpeg is a far-ranging library, supporting niche multimedia files, often through reverse-engineering. It is…
I was reading some papers. So, I thought I would use NotebookLM + some vibe coding to create this podcast style video. I will create 42 episodes to see if people find it useful. If not, I will stop. Enjoy!
🚀2D Gaussian Splatting: Real-Time, Geometry-Aware Radiance Field Reconstruction In this week’s deep dive, we unpack how 2D Gaussian Splatting (2DGS) redefines the future of real-time neural rendering and reconstruction. By collapsing volumetric 3D Gaussians into surface-aligned…
To really understand a concept, you have to "invent" it yourself in some capacity. Understanding doesn't come from passive content consumption. It is always self-built. It is an active, high-agency, self-directed process of creating and debugging your own mental models.
🚀From Blink to Think: Deploying ML on Arduino! At LearnOpenCV, we’ve always believed that AI shouldn’t be limited to powerful GPUs or cloud servers. It should run everywhere - even on the tiniest boards. Our latest article of the edge devices series, explores exactly that idea.…
That's spectacular. universityofcalifornia.edu/news/uc-wins-5…
Excited to release new repo: nanochat! (it's among the most unhinged I've written). Unlike my earlier similar repo nanoGPT which only covered pretraining, nanochat is a minimal, from scratch, full-stack training/inference pipeline of a simple ChatGPT clone in a single,…
📢VideoRAG: Redefining Long-Context Video Comprehension In this week’s deep dive, we explore another interesting approach for performing RAG on videos. VideoRAG, a groundbreaking framework that brings RAG to the world of extremely long videos. Unlike traditional LVLMs that…
San Diego Investors👇🏽 AI Beyond the Buzz: Smarter Money, Leaner Work, Safer Decisions in 2025 by Satya Mallick @LearnOpenCV Come on over to San Diego meeting this Saturday 11th October at 9am hosted by AAII San Diego!! <Details in link below> Want to know how AI is disrupting…
The more you show your 💜, the more we keep it coming every week :) +600 people have signed up for today's @SensAIHackademy hands-on workshop, starting in 2 hours, with Christoph Spinger from Ballee and @LearnOpenCV from @opencvlive ! And here is another interesting workshop…
📢The Ultimate Guide To VLM Evaluation Metrics, Datasets, And Benchmarks Evaluating Vision-Language Models (VLMs) is more than just checking accuracy. How would you know if your model understands a scene or is just hallucinating? Our new, comprehensive guide on LearnOpenCV…
The 3rd edition of my book Deep Learning with Python is being printed right now, and will be in bookstores within 2 weeks. You can order it now from Amazon or from Manning. This time, we're also releasing the whole thing as a 100% free website. I don't care if it reduces book…
📢 Getting Started with VLM on Jetson Nano Tiny Vision Language Models (VLMs) like Moondream2, LiquidAI’s LFM2-VL, Apple’s FastVLM, and Huggingface’s SmolVLM2 are bringing vision-language capabilities to the edge. In this tutorial, LearnOpenCV demonstrates how to deploy and run…
📢New Post Alert: 📙VLM on Edge Devices: Worth the Hype or Just a Novelty? The rise of Vision Language Models (VLMs) has been meteoric but can they really run effectively on edge devices? Our latest post is first in the series of experiments that we will continue for VLMs on…
📢AnomalyCLIP: Harnessing CLIP for Weakly-Supervised Video Anomaly Recognition In this week’s deep dive, we explore AnomalyCLIP, the first method to adapt CLIP’s vision–language latent space for Video Anomaly Recognition (VAR) under weak supervision. We break down how it learns…
📢AI for Video Understanding: From Content Moderation to Summarization In this blog post, we explore how to build a practical pipeline for AI-powered video understanding. We look at two main applications: video content moderation using CLIP and Gemini, and video summarization…
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