tensorflow-onnx
tflite2tensorflow
tensorflow-onnx | tflite2tensorflow | |
---|---|---|
7 | 2 | |
2,228 | 249 | |
1.7% | - | |
7.1 | 0.0 | |
about 1 month ago | over 1 year ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tensorflow-onnx
-
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
-
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.
-
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!
-
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?
-
💊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
tflite2tensorflow
-
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!
-
[D]Packaging machine learning service
What you are looking for has only been around for a month: https://github.com/PINTO0309/tflite2tensorflow
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
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.
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
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.
opti_models - PyTorch optimizations and benchmarking
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
tfjs-to-tf - A TensorFlow.js Graph Model Converter
infery-examples - A collection of demo-apps and inference scripts for various deep learning frameworks using infery (Python).
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving