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README.md

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 ## TI Edge-AI Microprocessor devices with C7 NPU
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 [![Models](https://img.shields.io/badge/Models-50%2B-brightgreen)](https://huggingface.co/TexasInstruments-EdgeAI) [![Hardware](https://img.shields.io/badge/Hardware-TI%20MPUs%20With%20C7%20NPU-brightgreen)](https://huggingface.co/TexasInstruments-EdgeAI) [![License](https://img.shields.io/badge/License-Open%20Source-brightgreen)](https://huggingface.co/TexasInstruments-EdgeAI)
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 **Bring Your Own Models and deploy on Texas Instruments Micro Processors with C7 NPU acceleration**
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 <details open>
 
-#### [TI Edge-AI Model Hub →](https://huggingface.co/TexasInstruments-EdgeAI)
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-edgeai-modelhub is a collection of example pre-trained AI models for TI edge devices — with easy scripts and config files for compilation, benchmark and deployment.
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-- Optimized for TI MPU devices
-- Benchmarked on real TI hardware
-- Portable across various TI MPU devices
-- Automated scripts for model compilation, benchmark & deployment
+#### [TI Edge-AI Model Hub](https://huggingface.co/TexasInstruments-EdgeAI)
 
-**Use cases:** Automotive · Aerospace & Defense · Industrial · Surveillance · Robotics · Edge IoT
-
-**Multiple model families and large collection of models** spanning image classification (ResNet, ViT, ConvNeXt, DINO/DINOv2, MobileNetV3) and object detection (YOLO11/26/v8/X, RT-DETRv2, RTMDet, RF-DETR, DETR & more)
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-**Validated on multiple SOCs** with C7 NPU accelerators — including AM62A, TDA4AEN/AM67A, TDA4VM, TDA4VE/TDA4VL/AM68A, and TDA4VH/AM69A device families.
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-[Browse](https://huggingface.co/TexasInstruments-EdgeAI) the full model catalog, docs & deployment guides
+Pre-trained AI models for TI edge devices, with scripts & configs for compilation, benchmark and deployment. Browse the full model catalog, docs & deployment guides.
 
+- Optimized, benchmarked on real hardware & portable across TI MPU devices; automated compile/benchmark/deploy scripts
+- Image classification (ResNet, ViT, ConvNeXt, DINO/DINOv2, MobileNetV3) & object detection (YOLO11/26/v8/X, RT-DETRv2, RTMDet, RF-DETR, DETR & more)
+- Validated on C7 NPU SOCs: AM62A, TDA4AEN/AM67A, TDA4VM, TDA4VE/TDA4VL/AM68A, TDA4VH/AM69A
+- **Use cases:** Automotive · Aerospace & Defense · Industrial · Surveillance · Robotics · Edge IoT
 </details>
 
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 ## TI MCU-AI Microcontrollers Devices
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 [![Devices](https://img.shields.io/badge/MCU%20Devices-MSP%20%7C%20AM13%20%7C%20C2000%20%7C%20Radar-brightgreen)](https://github.com/TexasInstruments/tinyml-modelzoo) [![GUI Tooling](https://img.shields.io/badge/GUI%20Tooling-Edge%20AI%20Studio-brightgreen)](https://www.ti.com/tool/EDGE-AI-STUDIO)
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 **Bring Your Own Data or Bring Your Own Models and deploy on TI microcontrollers, connectivity devices & radar sensors**
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 <details open>
 
-#### [Tiny ML ModelZoo →](https://github.com/TexasInstruments/tinyml-modelzoo)
-**tinyml-modelzoo** is TI's central repository for AI models, examples, and configurations for microcontroller (MCU) applications. Clone the repo, install it, and run any example config against your target device — training, quantization, and compilation happen automatically underneath.
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-- Ready-to-run examples, each with a dataset, a tuned model, and a device-specific config
-- Covers time-series classification, regression, forecasting & anomaly detection, plus audio, image & radar point-cloud classification
-- Supports AM13, MSPM0, C2000 (C28/C29 DSP), MSPM33C, AM26x, CC13xx/CC27xx/CC35xx connectivity devices, and IWRL6432 radar — several of them with a TinyEngine NPU accelerator
-- Automated training → quantization → compilation pipeline, no separate toolchain clone required
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-**Use cases:** Motor & fault diagnostics · Predictive maintenance · Keyword spotting · Presence & fall detection · Grid & power monitoring · Gesture recognition
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-[Browse tinyml-modelzoo](https://github.com/TexasInstruments/tinyml-modelzoo) example configs, supported devices & the training/compilation toolchain
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-[tinyml-tensorlab](https://github.com/TexasInstruments/tinyml-tensorlab) has additional command-line tools and Python packages for BYOD and BYOM workflows. 
-
-
-#### [Edge AI Studio GUI →](https://www.ti.com/tool/EDGE-AI-STUDIO)
-**Edge AI Studio** is TI's collection of graphical for edge AI development, part of the CCStudio™ tool ecosystem. It supports AI-accelerated devices (TinyEngine™ MCUs) as well as AI-supported devices without a dedicated accelerator.
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-- Model Composer: a no-code GUI for data collection, annotation, training & deployment
-- Bring Your Own Data (BYOD) to retrain TI Model Zoo models, or Bring Your Own Model (BYOM) via the command-line tools
-- Command-line tooling built on the TVM compiler framework, with TIDL for C7 NPU devices
-- Available as a cloud app for processors, and as cloud or desktop apps for MCUs, connectivity devices & radar sensors
-- Integrates with the CCStudio™ IDE
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-[Use or Download](https://www.ti.com/tool/EDGE-AI-STUDIO) the IDE, GUI & command-line tools for edge AI development
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+#### [Tiny ML ModelZoo](https://github.com/TexasInstruments/tinyml-modelzoo)
+TI's central repo of AI models, examples & configs for MCU applications. Clone, install, and run an example config against your target device — training, quantization & compilation happen automatically. Browse example configs, supported devices & the training/compilation toolchain.
+- Ready-to-run examples, each with a dataset, tuned model & device-specific config; no separate toolchain clone needed
+- Time-series classification/regression/forecasting/anomaly detection, plus audio, image & radar point-cloud classification
+- Devices: AM13, MSPM0, C2000 (C28/C29 DSP), MSPM33C, AM26x, CC13xx/CC27xx/CC35xx connectivity, IWRL6432 radar — several with a TinyEngine NPU accelerator
+- Use cases: Motor & fault diagnostics · Predictive maintenance · Keyword spotting · Presence & fall detection · Grid & power monitoring · Gesture recognition and more.
+- In addition, [tinyml-tensorlab](https://github.com/TexasInstruments/tinyml-tensorlab) has Python packages, agent skills and tools for BYOD/BYOM workflows.
+
+#### [Edge AI Studio GUI](https://www.ti.com/tool/EDGE-AI-STUDIO)
+TI's graphical & command-line tools for MCU AI development, part of the CCStudio™ ecosystem — for AI-accelerated MCU devices (TinyEngine™ MCUs) and non-accelerated devices alike. Use or Download the IDE for MCU AI development.
+- Model Composer: no-code GUI for data collection, annotation, training & deployment
+- BYOD to retrain TI Model Zoo models, or BYOM via command-line tools built on the TVM compiler framework (TIDL for C7 NPU devices)
+- Cloud app for processors; cloud or desktop app for MCUs, connectivity devices & radar sensors; integrates with the CCStudio™ IDE
 </details>
 
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