https://github.com/afondiel/Edge-AI-Model-Zoo
A list of production-ready models for resource-constrained devices.
https://github.com/afondiel/Edge-AI-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
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A list of production-ready models for resource-constrained devices.
- Host: GitHub
- URL: https://github.com/afondiel/Edge-AI-Model-Zoo
- Owner: afondiel
- License: apache-2.0
- Created: 2024-12-04T19:01:30.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-07-12T12:35:57.000Z (3 months ago)
- Last Synced: 2025-08-04T06:54:40.629Z (2 months 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: 913 KB
- Stars: 4
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
[](./CONTRIBUTING.md)
# Edge-AI Model Zoos
A curated list of Model Zoos & Hubs where you can find production-ready and optimized models for resource-constrained devices.
## Table of Contents
1. [Model Zoos & Hubs](#1-model-zoos--hubs)
2. [Model by Domain & Use Case](#2-model-by-domain--use-case)
3. [Resources](#resources)
- [How to choose the best model for Edge AI application](#how-to-choose-the-best-model-for-edge-ai-application)
- [Edge AI Technical Guide for Developers and Practitioners](#edge-ai-technical-guide-for-developers-and-practitioners)
## 1. Model Zoos & Hubs
[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/) |
| 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) |
| NVIDIA Pretrained AI Models (NGC + TAO)| Accelerate AI development with world-class customizable pretrained models from NVIDIA. | - [NVIDIA Pretrained AI Models - Main](https://developer.nvidia.com/ai-models)
- [NGC Model Catalog](https://catalog.ngc.nvidia.com/models?filters=&orderBy=weightPopularDESC&query=&page=&pageSize=)
- [TAO Model Zoo](https://docs.nvidia.com/tao/tao-toolkit/text/model_zoo/overview.html)|
| 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) |
| Qualcomm Models Zoo | A collection of AI models from Qualcomm. | [Qualcomm Models Zoo](https://github.com/quic/ai-hub-models/) |
| 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) |
| MediaPipe | Framework for building multimodal applied machine learning pipelines. | [MediaPipe](https://ai.google.dev/edge/mediapipe/solutions/guide) |
| TensorFlow Model Garden | A repository with a collection of TensorFlow models. | [TensorFlow Model Garden](https://github.com/tensorflow/models/tree/master) |
| Pytorch Model Zoo | A hub for pre-trained models on PyTorch framework. | [Pytorch Model Zoo](https://pytorch.org/serve/model_zoo.html) |
| 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/) |
| 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/) |
| 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) |
## 2. Model by Domain & Use Case
[Back to Table of Contents](#table-of-contents)
This is a non-exhaustive selection of models from several platforms listed in [Section 1](#1-model-zoos--hubs), ranged into six domains and a variety of tasks, with a focus on efficiency and real-world applications.
Domain
Task
Model
Description
Reference
Computer Vision
Object Detection
yolov8_det
Object detection for edge devices
YOLOv8 on GitHub
Image Classification
mobilenet_v3_small
Lightweight image classification
MobileNetV3 on TensorFlow Hub
Semantic Segmentation
deeplabv3_resnet50
Semantic image segmentation
DeepLabV3 on TensorFlow Hub
Instance Segmentation
yolov8_seg
Object detection and segmentation
YOLOv8 on GitHub
Object Tracking
DeepSort
Real-time object tracking
DeepSort on GitHub
Pose Estimation
openpose
Human pose estimation
OpenPose on GitHub
Facial Recognition
mediapipe_face
Face detection and recognition
MediaPipe Face on Google AI
Optical Character Recognition
trocr
Text recognition in images
TrOCR on Hugging Face
Video Classification
resnet_2plus1d
Video classification for action recognition
ResNet-2+1D on PyTorch Hub
Video Classification
resnet_3d
3D CNN for video classification
ResNet-3D on PyTorch Hub
Audio Processing
Speech-to-Text
distil-whisper
Lightweight speech recognition model
Distil-Whisper on Hugging Face
Sound Classification
audio-spectrogram-transformer
Transformer for audio classification
AST on Hugging Face
Voice Activity Detection
silero-vad
Voice activity detection for edge devices
Silero VAD on GitHub
Acoustic Scene Classification
panns
Audio tagging and scene classification
PANNS on GitHub
Speaker Diarization
pyannote-audio
Speaker diarization and segmentation
PyAnnote on Hugging Face
Speech Recognition
wav2vec2
Self-supervised speech representation learning
Wav2vec2 on Hugging Face
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Time Series
Predictive Maintenance
tsmixer
Time-series forecasting for maintenance
TimesFM on GitHub
Anomaly Detection
IsolationForest
Anomaly detection in time-series data
IsolationForest on Scikit-learn
Forecasting
informer
Transformer-based time-series forecasting
Informer on GitHub
Time-Series Classification
rocket
Efficient time-series classification
ROCKET on GitHub
Image Super-Resolution
real_esrgan_x4plus
Image super-resolution for temporal data
Real-ESRGAN on GitHub
Image Inpainting
lama_dilated
Image inpainting for time-series analysis
LaMa on GitHub
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NLP
Speech Recognition
Whisper
General-purpose speech recognition model
Whisper on Hugging Face
Keyword Spotting
silero-kws
Wake word detection for edge devices
Silero Models on GitHub
Text Classification
distilbert
Lightweight transformer for text classification
DistilBERT on Hugging Face
Named Entity Recognition
bert-ner
NER for entity extraction
BERT-NER on Hugging Face
Question Answering
mobilebert
Lightweight QA model for edge
MobileBERT on Hugging Face
Text Summarization
bart
Text summarization for short texts
BART on Hugging Face
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Generative AI
Image Generation & Synthesis
ControlNet
Fine control over image generation
ControlNet on GitHub
Stable Diffusion
Text-to-image generation
Stable Diffusion on Hugging Face
stylegan2
Image generation
StyleGAN2 on GitHub
Flux.1-schnell
Fast text-to-image generation
Flux.1 on Hugging Face, Awesome-Smol
Reve
Image generation with advanced text rendering
Reve on Hugging Face
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Small Language Model (SLM)
SmolLM2-1.7B
Small language model for efficient text generation
SmolLM2 on Hugging Face, Awesome-Smol
Gemma 2
Lightweight open model for text generation
Gemma 2 on Hugging Face, Awesome-Smol
Phi-3.5-mini
Small language model with strong reasoning
Phi-3.5-mini on Hugging Face, Awesome-Smol
Qwen2.5-1.5B
Efficient language model for instruction following
Qwen2.5 on Hugging Face, Awesome-Smol
Mixtral-8x22B
Sparse mixture of experts for text generation
Mixtral on Hugging Face
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Multimodality
SmolVLM-256M
Smallest vision-language model for image understanding
SmolVLM-256M on Hugging Face, Awesome-Smol
SmolVLM-500M
Vision-language model for image and text tasks
SmolVLM-500M on Hugging Face, Awesome-Smol
BakLLaVA-1
Multimodal model for text and image tasks
BakLLaVA-1 on Hugging Face, Awesome-Smol
PaliGemma
Vision-language model for multimodal tasks
PaliGemma on Hugging Face
Seed1.5-VL
Vision-language model with strong multimodal performance
Seed1.5-VL on Hugging Face
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Misc
Sensor Fusion
mediapipe_pose
Human pose estimation using sensor data
MediaPipe Pose on Google AI
Activity Recognition
har-cnn
Human activity recognition from sensor data
HAR-CNN on GitHub
Contextual Awareness
SmolVLM-256M
Multimodal model for environment understanding
SmolVLM-256M on Hugging Face, Awesome-Smol
Network Anomaly Detection
LOF
Local outlier factor for network anomalies
LOF on Scikit-learn
Device Behavior Anomaly
Autoencoder
Anomaly detection for device behavior
Keras Autoencoder
Sensor Data Anomaly
OC-SVM
One-class SVM for sensor data anomalies
OneClassSVM on Scikit-learn
On-device Control Systems
TD3
Twin Delayed DDPG for control systems
TD3 on GitHub
Various
pinecone
Vector database
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Various
weaviate-c2
Vector database
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Various
upstage
Various models
Awesome-Smol
## Resources
### How to choose the best model for an Edge AI application
Selecting the right model for edge deployment is critical for balancing **performance**, **accuracy** and **efficiency**.
### Why It Matters
- **Efficiency**: Edge devices (e.g., IoT, mobile, embedded systems) have limited compute, memory, and power.
- **Performance**: Real-time applications (e.g., autonomous drones, smart cameras) demand low latency and high accuracy.
- **Scalability**: The right model ensures cost-effective deployment across devices.
### Key Criteria
1. **Task Requirements**: Match the model to your application (e.g., vision, audio, multimodal).
2. **Hardware Constraints**: Consider compute (OPS), memory (MB), and energy (mWh) limits of your device.
3. **Performance Goals**: Balance accuracy, latency, and throughput for your use case.
4. **Deployment Ease**: Check compatibility with frameworks (e.g., TensorFlow Lite, ONNX).
**Next Steps**: Once you’ve shortlisted a model, use the [Edge AI Benchmarking Guide](https://github.com/afondiel/Edge-AI-Benchmarking) to **profile** and **optimize** the model performance.
### Edge AI Technical Guide for Developers and Practitioners
- [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering)
- [Edge AI Technical Guide](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/concepts)
- [Edge AI End-to-End Stack](https://www.qualcomm.com/developer/artificial-intelligence)
- [Edge AI Deployment Stack](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/concepts/deployment)
- [Edge AI Optimization Stack](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/concepts/optimization)
- [Edge AI Frameworks](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/frameworks)
- [Edge AI Model Zoos](https://github.com/afondiel/Edge-AI-Model-Zoo)
- [Edge AI Platforms](https://github.com/afondiel/Edge-AI-Platforms)
- [Edge AI Benchmarking](https://github.com/afondiel/Edge-AI-Benchmarking)
- [Edge AI Ecosystem](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/industry-applications)
- [Edge AI Books](https://github.com/afondiel/cs-books/blob/main/README.md#edge-computing)
- [Edge AI Blog](https://afondiel.github.io/posts/)
- [Edge AI Papers](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai/resources/edge_ai_papers_news.md)
[Back to Table of Contents](#table-of-contents)