Texas Instruments Edge AI

company

http://ti.com/edgeai

Activity Feed Request to join this org

AI & ML interests

None defined yet.

Recent Activity

mathmanu updated a Space about 10 hours ago

TexasInstruments-EdgeAI/README

mathmanu updated a model 1 day ago

TexasInstruments-EdgeAI/YOLOX-Detection

mathmanu updated a model 1 day ago

TexasInstruments-EdgeAI/YOLOv8-Detection

View all activity

Organization Card

Community About org cards

EdgeAI Model Hub

Pre-trained, hardware-optimized, edge AI models for TI Microprocessor devices

Overview

The TI EdgeAI Model Hub is a curated repository of open-source computer vision models optimized for deployment on Texas Instruments Microprocessor devices.

Models are compiled for TI hardware using edgeai-tidl-tools or edgeai-tidlrunner, enabling production-ready inference without cloud dependency. For more details on TIDL model compilation options, runtimes, and supported operators, see the TIDL User Guide.

✅ Portable across various devices
✅ Optimized for TI MPU devices
✅ Benchmarked on a variety of TI MPU devices with C7 NPU
✅ Automated scripts for model compilation, benchmark & deployment

Use Cases


Automotive	Aerospace & Defense	Industrial
Surveillance	Robotics	Edge IoT

License Summary

Models in this hub are distributed under various open-source licenses — each model's license is indicated in its own documentation page.

Disclaimer: Certain licenses in this repository impose distribution restrictions that may affect commercial, proprietary, or regulated-industry use. It is the sole responsibility of the user to review the applicable license terms, assess compatibility with their intended use, and obtain any necessary legal clearances prior to use or distribution. Texas Instruments makes no representation regarding the suitability of these licenses for any particular purpose and accepts no legal responsibility for the user's compliance obligations.

Supported Hardware

Compatible TI MPU device families compiled and validated via TIDL. See the supported devices, SDKs and version compatibility at the EdgeAI developer landing space and the edgeai-tidl-tools SDK version compatibility matrix.

Device Family	Variants
AM62A	AM62A3 · AM62A7
J722S	TDA4AEN · AM67A
J721E	TDA4VM
J721S2	TDA4VE · TDA4VL · TDA4AL · AM68A
J784S4	TDA4VH · TDA4AH · AM69A

Compilation & Deployment

Tool	Description
edgeai-tidlrunner	High-level compilation and benchmark interface. (Recommended for compilation and benchmark)
edgeai-tidl-tools	Deployment tools (and also low-level compilation tools for advanced users).

Quick Start

1. Clone the repository

git clone https://github.com/TexasInstruments/edgeai-modelhub.git
cd edgeai-modelhub

2. Navigate to a model directory and prepare the model

cd models/vision/<task>/<model>/
python prepare_model.py --model <variant>

3. Compile for TI hardware (run from inside the edgeai-tidlrunner directory)

cd /path/to/edgeai-tidlrunner
tidlrunner-cli compile --target_device <device> \
  --config_path /path/to/edgeai-modelhub/<model>_config.yaml

4. Infer on TI hardware (run from inside the edgeai-tidlrunner directory)

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device <device> \
  --config_path /path/to/edgeai-modelhub/<model>_config.yaml

Deployment

Deplyment can be done using edgeai-tidl-tools. For ONNX models, onnxruntime-tidl with TIDL acceleration can be used. Consult the documentation of edgeai-tidl-tools for more details.

Model Catalog

Model	Capability	Variants	Input	Performance	Docs
MobileNetV3	Image Classification	large	224×224	75.3% Top-1	View
ResNet-50	Image Classification	v1.5, v1	224×224	74.93–76.15% Top-1	View
DINO	Image Classification	ViT-S/16, ViT-S/8, ViT-B/16, ViT-B/8, ResNet-50	224×224	75.3–80.1% Top-1	View
DINOv2	Image Classification	ViT-S/14, ViT-B/14 (w/ & w/o registers)	224×224	80.9–84.6% Top-1	View
ViT	Image Classification	vit_b_16, vit_b_32, vit_l_16, vit_l_32	224×224	75.9–81.1% Top-1	View
ConvNeXt	Image Classification	convnext_tiny, convnext_small, convnext_base, convnext_large	224×224	82.5–84.4% Top-1	View
DEIMv2	Object Detection	s, m	640×640	50.9–53.0% mAP	View
DETR	Object Detection	detr_resnet50, detr_resnet50_dc5, detr_resnet101, detr_resnet101_dc5	800×800 (flexible)	AP50:95 42.0–44.9, AP50 62.4–64.7	View
Deformable-DETR	Object Detection	single-scale	800×800	AP50:95 39.4%	View
RF-DETR	Object Detection	nano, s, m, l	384–704px	48.4–56.5% mAP	View
RT-DETRv2	Object Detection	s, ms, m, l, x	640×640	48.1–54.3% mAP	View
RTMDet	Object Detection	tiny, s, m, l, x	640×640	40.9–52.8% mAP	View
YOLO11	Object Detection	n, s, m, l, x	640×640	39.5–54.7% mAP	View
YOLO26	Object Detection	n, s, m, l, x	640×640	40.9–57.5% mAP	View
YOLOv8	Object Detection	n, m	640×640	37.3–50.2% mAP	View
YOLOX	Object Detection	nano, tiny, m, l, x, darknet53	416×416 / 640×640	24.8–51.2% mAP	View