Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/afondiel/EdgeAI-Model-Zoo

A curated lists of small, optimized, and ready-to-deploy models for Edge-AI applications.
https://github.com/afondiel/EdgeAI-Model-Zoo
ai-acceleration aiot edge-ai edge-ai-models edge-computing edge-devices edge-impulse edge-model-zoo embedded-ai executorch iot litert mcus on-device-models openvino optimized-models qualcomm ready-to-deploy-models research-to-production-models tensorrt
Last synced: 11 days ago
JSON representation
A curated lists of small, optimized, and ready-to-deploy models for Edge-AI applications.
Host: GitHub
URL: https://github.com/afondiel/EdgeAI-Model-Zoo
Owner: afondiel
License: apache-2.0
Created: 2024-12-04T19:01:30.000Z (2 months ago)
Default Branch: main
Last Pushed: 2025-01-06T14:22:04.000Z (about 1 month ago)
Last Synced: 2025-01-17T20:04:25.238Z (23 days ago)
Topics: ai-acceleration, aiot, edge-ai, edge-ai-models, edge-computing, edge-devices, edge-impulse, edge-model-zoo, embedded-ai, executorch, iot, litert, mcus, on-device-models, openvino, optimized-models, qualcomm, ready-to-deploy-models, research-to-production-models, tensorrt
Homepage:
Size: 166 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project

awesome-smol - Edge AI Model Zoo - @afondiel
README

        [![](https://img.shields.io/badge/Contribute-Welcome-green)](./CONTRIBUTING.md)

# Edge-AI Model Zoo

## Overview

A curated list of pre-trained and ready-to-deploy models optimized for on-device/edge devices, available on AI hubs such as Hugging Face, GitHub Model Zoos, TensorFlow Hub, PyTorch Hub, and more.

## Table of Contents

- [Overview](#overview)

- [Model Zoo](#model-zoo)

- [Model Applications](#model-applications)

- [Resources](#resources)

## Model Zoo

[Back to Table of Contents](#table-of-contents)

| Model Zoo | Description | Links |

|-----------|-------------|------|

| Edge AI Labs Model Zoo | A collection of pre-trained, optimized models for low-power devices.| [EdgeAI Labs](https://edgeai.modelnova.ai/models/) |

| Edge Impulse Model Zoo | A repository of models optimized for edge devices. | [Edge Impulse Model Zoo](https://www.edgeimpulse.com/) |

| OpenVINO Model Zoo | A collection of pre-trained models ready for use with Intel's OpenVINO toolkit. | [OpenVINO Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) |

| LiteRT Pre-trained models | Pre-trained models optimized for Google's Lite Runtime. | [LiteRT Pre-trained Models](https://ai.google.dev/edge/litert/models/trained) |

| Keras Applications | Pre-trained models for Keras applications| [Keras Pre-trained Models](https://keras.io/api/applications/#available-models) |

| Qualcomm Models Zoo | A collection of AI models from Qualcomm. | [Qualcomm Models Zoo](https://github.com/quic/ai-hub-models/) |

| stm32ai-modelzoo | AI Model Zoo for STM32 microcontroller devices. | [stm32ai-modelzoo](https://github.com/STMicroelectronics/stm32ai-modelzoo/) |

| Model Zoo | A collection of pre-trained models for various machine learning tasks. | [Model Zoo](https://modelzoo.co/) |

| Hugging Face Models| A collection of pre-trained models for various machine learning tasks. | [Hugging Face Models](https://huggingface.co/models) |

| Papers with Code | A repository that links academic papers to their respective code and models. | [Papers with Code](https://paperswithcode.com/) |

| ONNX Model Zoo | A collection of pre-trained, state-of-the-art models in the ONNX format. | [ONNX Model Zoo](https://github.com/onnx/models) |

| TensorFlow Model Garden | A repository with a collection of TensorFlow models. | [TensorFlow Model Garden](https://github.com/tensorflow/models/tree/master) |

| MediaPipe | Framework for building multimodal applied machine learning pipelines. | [MediaPipe](https://ai.google.dev/edge/mediapipe/solutions/guide) |

| Pytorch Model Zoo | A hub for pre-trained models on PyTorch framework. | [Pytorch Model Zoo](https://pytorch.org/serve/model_zoo.html) |

| MXNet Model Zoo | A collection of pre-trained models for the Apache MXNet framework. | [MXNet Model Zoo](https://mxnet.apache.org/versions/1.1.0/model_zoo/index.html) |

| Deci’s Model Zoo | A curated list of high-performance deep learning models. | [Deci’s Model Zoo](https://deci.ai/modelzoo/) |

| NVIDIA Pretrained AI Models | Accelerate AI development with world-class customizable pretrained models from NVIDIA. | [NVIDIA Pretrained AI Models](https://developer.nvidia.com/ai-models) |

| Jetson Model Zoo and Community Projects | NVIDIA's collection of models and projects for Jetson platform. | [Jetson Model Zoo and Community Projects](https://developer.nvidia.com/embedded/community/jetson-projects) |

| Magenta | Models for music and art generation from Google's Magenta project. | [Magenta](https://github.com/magenta/magenta/tree/main/magenta/models/arbitrary_image_stylization) |

| Awesome-CoreML-Models Public | A collection of CoreML models for iOS developers. | [Awesome-CoreML-Models Public](https://github.com/likedan/Awesome-CoreML-Models) |

| Pinto Models | A variety of models for computer vision tasks. | [Pinto Models](https://github.com/PINTO0309/PINTO_model_zoo) |

| Baidu AI Open Model Zoo | Baidu's collection of AI models. | [Baidu AI Open Model Zoo](https://ai.baidu.com/tech/modelzoo) |

| Hailo Model Zoo | A set of models optimized for Hailo's AI processors. | [Hailo Model Zoo](https://github.com/hailo-ai/hailo_model_zoo) |

## Model Applications

[Back to Table of Contents](#table-of-contents)

The table below categorizes some of these models based on their primary capabilities for real-world applications:

| Category | Model | Description | Reference |

|---|---|---|---|

| **Language** | Whisper | General-purpose speech recognition model | [Whisper on Hugging Face](https://huggingface.co/openai/whisper) |

| | Baichuan | Large language model | [Baichuan on Hugging Face](https://huggingface.co/baichuan-inc/Baichuan-13B-Base) |

| | huggingface_wavlm_base_plus | Audio language model for speech recognition | [WavLM on Hugging Face](https://huggingface.co/microsoft/wavlm-base-plus) |

| | whisper_asr | Speech recognition model | [Whisper ASR on GitHub](https://github.com/openai/whisper) |

| | trocr | Text recognition in images | [TrOCR on Hugging Face](https://huggingface.co/microsoft/trocr-base) |

| |MobileLLM | Large language model | [MobileLLM on Hugging Face](https://huggingface.co/collections/facebook/mobilellm-6722be18cb86c20ebe113e95)|

| **Audio** | Whisper | Speech-to-text | [Whisper on Hugging Face](https://huggingface.co/openai/whisper) |

| | huggingface_wavlm_base_plus | Audio language model for speech recognition | [WavLM on Hugging Face](https://huggingface.co/microsoft/wavlm-base-plus) |

| | whisper_asr | Speech recognition model | [Whisper ASR on GitHub](https://github.com/openai/whisper) |

| **Vision** | aotgan | Image generation | [AOT-GAN on GitHub](https://github.com/researchmm/AOT-GAN) |

| | convnext_tiny | Vision transformer for image classification | [ConvNeXt on Hugging Face](https://huggingface.co/facebook/convnext-tiny-224) |

| | ddrnet23_slim | Image segmentation | [DDRNet on GitHub](https://github.com/ydhongHIT/DDRNet) |

| | deeplabv3_resnet50 | Semantic image segmentation | [DeepLabV3 on TensorFlow Hub](https://tfhub.dev/tensorflow/deeplabv3/1) |

| | densenet121 | Image classification | [DenseNet on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_densenet/) |

| | detr_resnet101 | Object detection | [DETR on GitHub](https://github.com/facebookresearch/detr) |

| | detr_resnet101_dc5 | Object detection | [DETR on GitHub](https://github.com/facebookresearch/detr) |

| | detr_resnet50 | Object detection | [DETR on GitHub](https://github.com/facebookresearch/detr) |

| | detr_resnet50_dc5 | Object detection | [DETR on GitHub](https://github.com/facebookresearch/detr) |

| | esrgan | Image super-resolution | [ESRGAN on GitHub](https://github.com/xinntao/ESRGAN) |

| | facebook_denoiser | Image denoising | [Denoiser on GitHub](https://github.com/facebookresearch/denoiser) |

| | fcn_resnet50 | Semantic image segmentation | [FCN on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_fcn_resnet50/) |

| | googlenet | Image classification | [GoogLeNet on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_googlenet/) |

| | lama_dilated | Image inpainting | [LaMa on GitHub](https://github.com/saic-mdal/lama) |

| | litehrnet | Image segmentation | [Lite-HRNet on GitHub](https://github.com/HRNet/Lite-HRNet) |

| | mediapipe_face | Face detection | [MediaPipe Face on Google AI](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html) |

| | mediapipe_hand | Hand detection | [MediaPipe Hand on Google AI](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html) |

| | mnasnet05 | Image classification | [MnasNet on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_mnasnet/) |

| | mobiledet | Object detection | [MobileDet on TensorFlow Hub](https://tfhub.dev/s?q=mobiledet) |

| | mobilenet_v3_small | Image classification | [MobileNetV3 on TensorFlow Hub](https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5) |

| | openpose | Human pose estimation | [OpenPose on GitHub](https://github.com/CMU-Perceptual-Computing-Lab/openpose) |

| | QuickSRNet_Large | Image super-resolution | [QuickSRNet on GitHub](https://github.com/QuickSRNet/QuickSRNet) |

| | real_esrgan_general_x4v3 | Image super-resolution | [Real-ESRGAN on GitHub](https://github.com/xinntao/Real-ESRGAN) |

| | real_esrgan_x4plus | Image super-resolution | [Real-ESRGAN on GitHub](https://github.com/xinntao/Real-ESRGAN) |

| | resnet_2plus1d | Video classification | [ResNet-2+1D on PyTorch Hub](https://pytorch.org/hub/facebookresearch_pytorchvideo_resnet/) |

| | resnet_3d | Video classification | [ResNet-3D on PyTorch Hub](https://pytorch.org/hub/facebookresearch_pytorchvideo_resnet/) |

| | ResNeXt50 | Image classification | [ResNeXt on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_resnext/) |

| | sam | Segmentation | [SAM on GitHub](https://github.com/facebookresearch/segment-anything) |

| | Sesr_m3 | Image super-resolution | [SESR on GitHub](https://github.com/SESR/SESR) |

| | shufflenet_v2 | Image classification | [ShuffleNetV2 on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_shufflenet_v2/) |

| | sinet | Image segmentation | [SINet on GitHub](https://github.com/DengPingFan/SINet) |

| | squeezenet_1 | Image classification | [SqueezeNet on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_squeezenet/) |

| | stylegan2 | Image generation | [StyleGAN2 on GitHub](https://github.com/NVlabs/stylegan2) |

| | unet_segmentation | Image segmentation | [UNet on GitHub](https://github.com/milesial/Pytorch-UNet) |

| | vit | Image classification | [ViT on Hugging Face](https://huggingface.co/google/vit-base-patch16-224) |

| | wideresnet50 | Image classification | [WideResNet on PyTorch Hub](https://pytorch.org/hub/pytorch_vision_wideresnet/) |

| | xisr | Image super-resolution | [XISR on GitHub](https://github.com/XISR/XISR) |

| | yolov6 | Object detection | [YOLOv6 on GitHub](https://github.com/meituan/YOLOv6) |

| | yolov7 | Object detection | [YOLOv7 on GitHub](https://github.com/WongKinYiu/yolov7) |

| | yolov8_det | Object detection | [YOLOv8 on GitHub](https://github.com/ultralytics/yolov8) |

| | yolov8_seg | Object detection and segmentation | [YOLOv8 on GitHub](https://github.com/ultralytics/yolov8) |

| **Multimodality** | ControlNet | Fine control over image generation | [ControlNet on GitHub](https://github.com/lllyasviel/ControlNet) |

| | Stable Diffusion | Text-to-image generation | [Stable Diffusion on Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4) |

| | Mediapipe_pose | Human pose estimation | [MediaPipe Pose on Google AI](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html) |

| **Other** | pinecone | Vector database | Pinecone [invalid URL removed] |

| | weaviate-c2 | Vector database | Weaviate [invalid URL removed] |

| | Intel | Various models | Intel AI [invalid URL removed] |

| | kakao-enterprise | Various models | Hugging Face [invalid URL removed] |

| | laion | Various models | Hugging Face [invalid URL removed] |

| | openai | Various models | OpenAI [invalid URL removed] |

| | runwayml | Various models | Hugging Face [invalid URL removed] |

| | Salesforce | Various models | Hugging Face [invalid URL removed] |

| | stabilityai | Various models | Hugging Face [invalid URL removed] |

| | upstage | Various models | Hugging Face [invalid URL removed] |

| | ybelkada | Various models | Hugging Face [invalid URL removed] |

## Resources

[Back to Table of Contents](#table-of-contents)

**Model Evaluation & Benchmarking**: 

- **[Model Evaluation & Benchmarking Guide](./model-eval-benchmarking-guide.md)**

**Edge-AI Hardware Platforms:**

- [Edge-AI Hardware Platform List](https://github.com/afondiel/Edge-AI-Platforms)

- [NVIDIA Jetson Orin Nano announcement](https://blogs.nvidia.com/blog/jetson-generative-ai-supercomputer/)

**Deployment Frameworks/Toolkits/Platforms**

- [LiteRT (formerly TensorFlow Lite)](https://www.tensorflow.org/lite)

    - [TFLite Model Maker](https://www.tensorflow.org/lite/models/modify/model_maker)

- [Core ML](https://developer.apple.com/documentation/coreml)

- [Pytorch Edge](https://pytorch.org/edge)

- [ONNX Runtime](https://onnxruntime.ai/)

- [TensorRT](https://developer.nvidia.com/tensorrt)

- [OpenVINO Toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html)

- [Google Cloud AI Platform](https://cloud.google.com/ai-platform)

- [AWS SageMaker Neo](https://aws.amazon.com/sagemaker/neo/)

- [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/)

- [Edge Impulse](https://www.edgeimpulse.com/)

- [Apache TVM](https://tvm.apache.org/)

- [MLIR](https://mlir.llvm.org/)

- [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server)

**Companies developing AI solutions at the Edge**

- [Intel](https://www.intel.com/content/www/us/en/artificial-intelligence/overview.html)

- [NVIDIA](https://www.nvidia.com/en-us/research/)

- [AMD](https://www.amd.com/en/solutions/healthcare/edge/ai.html)

- [ARM](https://www.arm.com/solutions/artificial-intelligence)

- [STMicroelectronics](https://www.st.com/content/st_com/en/stm32-ai.html)

- [Qualcomm](https://www.qualcomm.com/research/artificial-intelligence)

- [Samsung AI](https://research.samsung.com/artificial-intelligence)

- [MediaTek](https://corp.mediatek.com)

- [Hugging Face](https://huggingface.co/)

- [Meta (Facebook)](https://ai.facebook.com/)

    - [Facebook AI Research (FAIR) Models](https://github.com/facebookresearch)

- [Microsoft (MS)](https://www.microsoft.com/en-us/research/)

- [Edge Impulse](https://www.edgeimpulse.com/)

- [IBM Model Asset Exchange](https://developer.ibm.com/exchanges/models/)

- [Google AI Hub](https://aihub.cloud.google.com/)

    - [Google/Android](https://ai.google.dev/edge)

- [Apple](https://www.apple.com/ai/)

- [Amazon Web Services (AWS)](https://aws.amazon.com/machine-learning/)

- [Azure AI Gallery](https://gallery.azure.ai/)

- [Baidu AI Open Model Zoo](https://ai.baidu.com/tech/modelzoo)

- [Alibaba Cloud AI Model Marketplace](https://www.alibabacloud.com/solutions/ai)

- [Tencent AI Open Platform](https://ai.qq.com/)

- [HAILO](https://hailo.ai/)

**Developer Resources**

- NVIDIA: 

    - https://developer.nvidia.com/

    - https://docs.nvidia.com/

- AMD:

    - https://www.amd.com/en/products/adaptive-socs-and-fpgas/versal/gen2/ai-edge-series.html

    - https://www.amd.com/en/solutions/healthcare/edge/ai.html

- GOOGLE DEVELOPER:

    - Google AI Edge team: https://developers.googleblog.com/en/search/?product_categories=AI+Edge 

- https://community.arm.com/arm-community-blogs/b/ai-and-ml-blog/posts/optimizing-ai-models-for-arm-ethos-u-npus-using-the-nvidia-tao-toolkit

- https://www.nota.ai/community/integrating-launchx-with-nvidia-tao-toolkit-for-running-on-various-edge-devices

- https://www.hackster.io/sandeep-mistry/nvidia-tao-object-detection-ml-models-on-arm-based-devices-3e51fb/

- https://blog.st.com/tao-toolkit/

[Back to Table of Contents](#table-of-contents)

**Blogs:**

- [13 Free Resources and Model Zoos for Deep Learning and Computer Vision Models](https://www.edge-ai-vision.com/2022/04/13-free-resources-and-model-zoos-for-deep-learning-and-computer-vision-models/)

- [LiteRT Models](https://ai.google.dev/edge/litert/models/trained)

- [Amazon SageMaker and Qualcomm AI Hub](https://aws.amazon.com/blogs/machine-learning/train-optimize-and-deploy-models-on-edge-devices-using-amazon-sagemaker-and-qualcomm-ai-hub/)

- [Hugging Face Computer Vision Course](https://huggingface.co/learn/computer-vision-course/unit9/intro_to_model_optimization)

- [Apple Foundation Models](https://machinelearning.apple.com/research/introducing-apple-foundation-models)

- [MediaTek Integrates NVIDIA TAO ToolKit with NeuroPilot SDK for Accelerated Development of Edge AI Applications in IoT](https://corp.mediatek.com/news-events/press-releases/mediatek-integrates-nvidia-tao-toolkit-with-neuropilot-sdk-for-accelerated-development-of-edge-ai-applications-in-iot)