tensorflow-onnx
ailia-models
| tensorflow-onnx | ailia-models | |
|---|---|---|
| 7 | 4 | |
| 2,214 | 1,825 | |
| 2.0% | 4.7% | |
| 7.1 | 9.8 | |
| 12 days ago | 3 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | - |
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tensorflow-onnx
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Operationalize TensorFlow Models With ML.NET
The easiest way to transform the downloaded TensorFlow model to an ONNX model is to use the tool tf2onnx from https://github.com/onnx/tensorflow-onnx
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Which models can be converted to ONNX?
But I found that there are limitation in practice. For instance, I found that the conversion of this model to ONNX fails when using tf2onnx.
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10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
They come in the form of tflite models, so I had to convert them to onnx. I used tf2onnx for converting the pose landmark model and tflite2tensorflow for converting the pose detection model. For improving performance, I had created a small script which modified the landmark models for supporting batch inference. This script is not included in the repository, but do tell me if you need it!
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Auto Annotation using ONNX and YOLOv7 model (Object Detection)
pb to ONNX Follow tensorflow-onnx:- https://github.com/onnx/tensorflow-onnx
- Can you inference a .tflite model file using Pytorch mobile?
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💊Your daily dose of machine learning : converting deep learning models to ONNX format
You can learn more about this tool on their github repo : https://github.com/onnx/tensorflow-onnx
ailia-models
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10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
If you're looking for the differences in terms of how inference is done, I recommend you take a look at MediaPipe's source code. MediaPipe doesn't use raw code, but uses a "graph" instead (eg. pose_landmark_cpu.pbtxt), which can be visualised using MediaPipe Viz. I also used axinc-ai/ailia-models as the base (preprocessing, inference, postprocessing, etc...) which I further built upon (keypoint refinement, roi from keypoints, filtering / smoothing, etc...)
- [P] A collection of pre-trained, state-of-the-art AI models
- Ailia-models: A collection of pre-trained, state-of-the-art AI models
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
DeepCamera - Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Put-In-Context - Putting Visual Object Recognition in Context
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
robotics-level-4 - This repo contains projects created using TensorFlow-Lite on Raspberry Pi and Teachable Machine. AI and ML capabilities have been integrated with Robot's software.
CFU-Playground - Want a faster ML processor? Do it yourself! -- A framework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers (TFLM). . . . . . Online tutorial: https://google.github.io/CFU-Playground/ For reference docs, see the link below.
mlapi - An easy to use/extend object recognition API you can locally install. Python+Flask. Also works with ZMES!
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
PeekingDuck - A modular framework built to simplify Computer Vision inference workloads.
infery-examples - A collection of demo-apps and inference scripts for various deep learning frameworks using infery (Python).
Video-Dataset-Loading-Pytorch - Generic PyTorch dataset implementation to load and augment VIDEOS for deep learning training loops.