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DeepSeek OCR Paper Explanation and Test using Transformers and vLLM Pipeline. Understanding Context Optical Compression and model architecture in depth.
Discover how 2D Gaussian Splatting transforms neural rendering by replacing volumetric 3D Gaussians with surface-aligned 2D disks.
Models with billions, or trillions, of parameters are becoming the norm. These models can write essays, generate code, as well as create art. But they can still get stuck on
Deploying ML on Arduino Nano 33 BLE. Explore TinyML techniques, setup steps, and why older Arduinos still rival the new Arduino Uno Q.
Discover VideoRAG, a framework that fuses graph-based reasoning and multi-modal retrieval to enhance LLMs' ability to understand multi-hour videos efficiently.
Learn how to build AI agent from scratch using Moondream3 and Gemini. It is a generic task based agent free from application APIs.
Get a comprehensive overview of VLM Evaluation Metrics, Benchmarks and various datasets for tasks like VQA, OCR and Image Captioning.
Learn how to setup a pipeline to run VLM on Jetson Nano using Huggingface Transformers. Run models like LiquidAI, Moondream2, FastVLM, and SmolVLM.
Testing Vision Language Models (VLM) on edge devices. Check how small VLMs perform on our custom Raspberry Pi Cluster and Jetson Nanos.
Video Anomaly Detection (VAD) is one of the most challenging problems in computer vision. It involves identifying rare, abnormal events in videos – such as burglary, fighting, or accidents –
The rapid growth of video content has created a need for advanced systems to process and understand this complex data. Video understanding is a critical field in AI, where the
Learn how Video-RAG boosts training-free and low-compute long-video understanding by pairing OCR, ASR, and open-vocabulary detection with any long-video LVLMs.
