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| title: TIEdgeAI | |
| emoji: π€ | |
| colorFrom: red | |
| colorTo: gray | |
| sdk: static | |
| pinned: false | |
| <!-- AUTO-GENERATED by packaging/generate_org_card.py from README.md β do not edit by hand. Edit README.md and re-run the generator instead. --> | |
| <div align="center"> | |
| <img src="https://huggingface.co/spaces/TexasInstruments/README/resolve/main/docs/assets/TXN-Logo.png" alt="Texas Instruments" width="72" height="72" /> | |
| # EdgeAI Model Hub | |
| **Pre-trained, hardware-optimized, edge AI models for TI Microprocessor devices** | |
| </div> | |
| --- | |
| ## 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). | |
| - β Portable across various devices | |
| - β Optimized for TI MPU devices | |
| - β Benchmarked on a variety of TI MPU devices with C7 NPU | |
| - β 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 β 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) Β· [AM62A7](https://www.ti.com/product/AM62A7) | | |
| | **J722S** | [TDA4AEN](https://www.ti.com/product/TDA4AEN-Q1) Β· [AM67A](https://www.ti.com/product/AM67A) | | |
| | **J721E** | [TDA4VM](https://www.ti.com/product/TDA4VM) | | |
| | **J721S2** | [TDA4VE](https://www.ti.com/product/TDA4VE-Q1) Β· [TDA4VL](https://www.ti.com/product/TDA4VL-Q1) Β· [TDA4AL](https://www.ti.com/product/TDA4AL-Q1) Β· [AM68A](https://www.ti.com/product/AM68A) | | |
| | **J784S4** | [TDA4VH](https://www.ti.com/product/TDA4VH-Q1) Β· [TDA4AH](https://www.ti.com/product/TDA4AH-Q1) Β· [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/<task>/<model>/ | |
| python prepare_model.py --model <variant> | |
| ``` | |
| **3. Compile for TI hardware** (run from inside the edgeai-tidlrunner directory) | |
| ```bash | |
| 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) | |
| ```bash | |
| 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](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 | Reference Performance | License | Repo | | |
| |---|---|---|---|---|---|---| | |
| | **MobileNetV3** | Image Classification | large | 224Γ224 | 75.3% Top-1 | [](https://opensource.org/licenses/BSD-3-Clause) | [View](https://huggingface.co/TexasInstruments/MobileNetV3-Classification) | | |
| | **ResNet-50** | Image Classification | v1.5, v1 | 224Γ224 | 74.93β76.15% Top-1 | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/ResNet-Classification) | | |
| | **DINO** | Image Classification | ViT-S/16, ViT-S/8, ViT-B/16, ViT-B/8, ResNet-50 | 224Γ224 | 75.3β80.1% Top-1 | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/DINO-Classification) | | |
| | **DINOv2** | Image Classification | ViT-S/14, ViT-B/14 (w/ & w/o registers) | 224Γ224 | 80.9β84.6% Top-1 | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/DINOv2-Classification) | | |
| | **ViT** | Image Classification | vit_b_16, vit_b_32, vit_l_16, vit_l_32 | 224Γ224 | 75.9β81.1% Top-1 | [](https://opensource.org/licenses/BSD-3-Clause) | [View](https://huggingface.co/TexasInstruments/ViT-Classification) | | |
| | **ConvNeXt** | Image Classification | convnext_tiny, convnext_small, convnext_base, convnext_large | 224Γ224 | 82.5β84.4% Top-1 | [](https://opensource.org/licenses/BSD-3-Clause) | [View](https://huggingface.co/TexasInstruments/ConvNeXt-Classification) | | |
| | **DEIMv2** | Object Detection | s, m | 640Γ640 | 50.9β53.0% mAP | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/DEIMv2-Detection) | | |
| | **DETR** | Object Detection | detr_resnet50, detr_resnet50_dc5, detr_resnet101, detr_resnet101_dc5 | 800Γ800 (flexible) | AP50:95 42.0β44.9, AP50 62.4β64.7 | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/DETR-Detection) | | |
| | **Deformable-DETR** | Object Detection | single-scale | 800Γ800 | AP50:95 39.4% | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/Deformable-DETR-Detection) | | |
| | **RF-DETR** | Object Detection | nano, s, m, l | 384β704px | 48.4β56.5% mAP | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/RF-DETR-Detection) | | |
| | **RT-DETRv2** | Object Detection | s, ms, m, l, x | 640Γ640 | 48.1β54.3% mAP | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/RT-DETRv2-Detection) | | |
| | **RTMDet** | Object Detection | tiny, s, m, l, x | 640Γ640 | 40.9β52.8% mAP | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/RTMDet-Detection) | | |
| | **YOLO11** | Object Detection | n, s, m, l, x | 640Γ640 | 39.5β54.7% mAP | [](https://www.gnu.org/licenses/agpl-3.0.html) | [View](https://huggingface.co/TexasInstruments/YOLO11-Detection) | | |
| | **YOLO26** | Object Detection | n, s, m, l, x | 640Γ640 | 40.9β57.5% mAP | [](https://www.gnu.org/licenses/agpl-3.0.html) | [View](https://huggingface.co/TexasInstruments/YOLO26-Detection) | | |
| | **YOLOv8** | Object Detection | n, m | 640Γ640 | 37.3β50.2% mAP | [](https://www.gnu.org/licenses/agpl-3.0.html) | [View](https://huggingface.co/TexasInstruments/YOLOv8-Detection) | | |
| | **YOLOX** | Object Detection | nano, tiny, m, l, x, darknet53 | 416Γ416 / 640Γ640 | 24.8β51.2% mAP | [](https://www.apache.org/licenses/LICENSE-2.0) | [View](https://huggingface.co/TexasInstruments/YOLOX-Detection) | | |
| --- | |
| ## Resources & Links | |
| - **Ecosystem:** [TI EdgeAI](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu) Β· [EdgeAI SDK](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu/readme_sdk.md) | |
| - **Tools:** [edgeai-tidlrunner](https://github.com/TexasInstruments/edgeai-tidlrunner) Β· [edgeai-tidl-tools](https://github.com/TexasInstruments/edgeai-tidl-tools) | |
| - **Community:** [E2E Support](https://e2e.ti.com/support/processors-group/processors/f/processors-forum) Β· [Issues](https://github.com/TexasInstruments/edgeai/issues) Β· [Discussions](https://github.com/TexasInstruments/edgeai/discussions) | |
| --- | |
| <div align="center"> | |
| **Maintained by Texas Instruments EdgeAI Team | Last Updated August 2026** | |
| [Contact](mailto:edgeai-dev@list.ti.com) | |
| </div> | |