--- title: TexasInstruments emoji: 🤖 colorFrom: red colorTo: gray sdk: static pinned: false ---
Texas Instruments # 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](https://github.com/TexasInstruments/edgeai-tidl-tools) or [edgeai-tidlrunner](https://github.com/TexasInstruments/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](https://github.com/TexasInstruments/edgeai-tidl-tools#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](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu/readme_sdk.md) and the [edgeai-tidl-tools SDK version compatibility matrix](https://github.com/TexasInstruments/edgeai-tidl-tools/blob/master/docs/sdk_version_compatibility_table.md). | Device Family | Variants | |---|---| | **AM62A** | [AM62A3](https://www.ti.com/product/AM62A3) · [AM62A7](https://www.ti.com/product/AM62A7) | | **J722S** | [TDA4AEN](https://www.ti.com/product/TDA4AEN-Q1) · [AM67A](https://www.ti.com/product/AM67A) | | **J721E** | [TDA4VM](https://www.ti.com/product/TDA4VM) | | **J721S2** | [TDA4VE](https://www.ti.com/product/TDA4VE-Q1) · [TDA4VL](https://www.ti.com/product/TDA4VL-Q1) · [TDA4AL](https://www.ti.com/product/TDA4AL-Q1) · [AM68A](https://www.ti.com/product/AM68A) | | **J784S4** | [TDA4VH](https://www.ti.com/product/TDA4VH-Q1) · [TDA4AH](https://www.ti.com/product/TDA4AH-Q1) · [AM69A](https://www.ti.com/product/AM69A) | ## Compilation & Deployment | Tool | Description | |------|-------------| | **[edgeai-tidlrunner](https://github.com/TexasInstruments/edgeai-tidlrunner)** | High-level compilation and benchmark interface. (Recommended for compilation and benchmark) | | **[edgeai-tidl-tools](https://github.com/TexasInstruments/edgeai-tidl-tools)** | Deployment tools (and also low-level compilation tools for advanced users). | ## Quick Start **1. Clone the repository** ```bash git clone https://github.com/TexasInstruments/edgeai-modelhub.git cd edgeai-modelhub ``` **2. Navigate to a model directory and prepare the model** ```bash cd models/vision/// python prepare_model.py --model ``` **3. Compile for TI hardware** (run from inside the edgeai-tidlrunner directory) ```bash cd /path/to/edgeai-tidlrunner tidlrunner-cli compile --target_device \ --config_path /path/to/edgeai-modelhub/_config.yaml ``` **4. Infer on TI hardware** (run from inside the edgeai-tidlrunner directory) ```bash cd /path/to/edgeai-tidlrunner tidlrunner-cli infer --target_device \ --config_path /path/to/edgeai-modelhub/_config.yaml ``` ## Deployment Deplyment can be done using **[edgeai-tidl-tools](https://github.com/TexasInstruments/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 | License | Docs | |---|---|---|---|---|---|---| | **MobileNetV3** | Image Classification | large | 224×224 | 75.3% Top-1 | [![BSD-3-Clause](https://img.shields.io/badge/BSD--3--Clause-065f46?style=flat-square)](https://opensource.org/licenses/BSD-3-Clause) | [View](models/vision/classification/MobileNetV3/) | | **ResNet-50** | Image Classification | v1.5, v1 | 224×224 | 74.93–76.15% Top-1 | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/classification/ResNet/) | | **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 | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/classification/DINO/) | | **DINOv2** | Image Classification | ViT-S/14, ViT-B/14 (w/ & w/o registers) | 224×224 | 80.9–84.6% Top-1 | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/classification/DINOv2/) | | **ViT** | Image Classification | vit_b_16, vit_b_32, vit_l_16, vit_l_32 | 224×224 | 75.9–81.1% Top-1 | [![BSD-3-Clause](https://img.shields.io/badge/BSD--3--Clause-065f46?style=flat-square)](https://opensource.org/licenses/BSD-3-Clause) | [View](models/vision/classification/ViT/) | | **ConvNeXt** | Image Classification | convnext_tiny, convnext_small, convnext_base, convnext_large | 224×224 | 82.5–84.4% Top-1 | [![BSD-3-Clause](https://img.shields.io/badge/BSD--3--Clause-065f46?style=flat-square)](https://opensource.org/licenses/BSD-3-Clause) | [View](models/vision/classification/ConvNeXt/) | | **DEIMv2** | Object Detection | s, m | 640×640 | 50.9–53.0% mAP | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/DEIMv2/) | | **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 | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/DETR/) | | **Deformable-DETR** | Object Detection | single-scale | 800×800 | AP50:95 39.4% | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/Deformable-DETR/) | | **RF-DETR** | Object Detection | nano, s, m, l | 384–704px | 48.4–56.5% mAP | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/RF-DETR/) | | **RT-DETRv2** | Object Detection | s, ms, m, l, x | 640×640 | 48.1–54.3% mAP | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/RT-DETRv2/) | | **RTMDet** | Object Detection | tiny, s, m, l, x | 640×640 | 40.9–52.8% mAP | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/RTMDet/) | | **YOLO11** | Object Detection | n, s, m, l, x | 640×640 | 39.5–54.7% mAP | [![AGPL 3.0](https://img.shields.io/badge/AGPL%203.0-9d174d?style=flat-square)](https://www.gnu.org/licenses/agpl-3.0.html) | [View](models/vision/detection/YOLO11/) | | **YOLO26** | Object Detection | n, s, m, l, x | 640×640 | 40.9–57.5% mAP | [![AGPL 3.0](https://img.shields.io/badge/AGPL%203.0-9d174d?style=flat-square)](https://www.gnu.org/licenses/agpl-3.0.html) | [View](models/vision/detection/YOLO26/) | | **YOLOv8** | Object Detection | n, m | 640×640 | 37.3–50.2% mAP | [![AGPL 3.0](https://img.shields.io/badge/AGPL%203.0-9d174d?style=flat-square)](https://www.gnu.org/licenses/agpl-3.0.html) | [View](models/vision/detection/YOLOv8/) | | **YOLOX** | Object Detection | nano, tiny, m, l, x, darknet53 | 416×416 / 640×640 | 24.8–51.2% mAP | [![Apache 2.0](https://img.shields.io/badge/Apache%202.0-1d4ed8?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) | [View](models/vision/detection/YOLOX/) | ## Resources & Links - **Ecosystem:** [TI EdgeAI](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu) · [EdgeAI SDK](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu/readme_sdk.md) - **Tools:** [edgeai-tidlrunner](https://github.com/TexasInstruments/edgeai-tidlrunner) · [edgeai-tidl-tools](https://github.com/TexasInstruments/edgeai-tidl-tools) - **Community:** [E2E Support](https://e2e.ti.com/support/processors-group/processors/f/processors-forum) · [Issues](https://github.com/TexasInstruments/edgeai/issues) · [Discussions](https://github.com/TexasInstruments/edgeai/discussions) ---
Maintained by Texas Instruments EdgeAI Team  |  Last Updated August 2026 [Contact](mailto:edgeai-dev@list.ti.com)