labml
tensorflow-onnx
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labml | tensorflow-onnx | |
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23 | 7 | |
1,867 | 2,214 | |
4.3% | 2.0% | |
9.7 | 7.1 | |
3 days ago | 10 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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labml
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Creating stickers using SD with img2img
Used the PromptArt app by labml.ai to generate a sticker of an image I took from my iPhone. The results are amazing.
- [D] Why doesnโt your team use an experiment tracking tool?
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Probe PyTorch models
๐ป Github
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[P] Probe PyTorch models
๐งโ๐ซ Demo that extracts attention maps of BERT
- Show HN: Probe PyTorch Models
- [D] How do you guys tune hyperparameters, when a single training run takes a long time (days to weeks)?
- Machine Learning Best Practices
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[D] Machine Learning Best Practices
from github
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[P] Annotated deep learning paper implementations
labmlai/labml is a set of tools (tracking experiments, configurations, a bunch of helpers) we coded to ease our ML work (which later improved and open sourced). So we use it in all our projects because it makes things easier for us.
- React's UI State Model vs. Vanilla JavaScript
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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๐Your 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
What are some alternatives?
nn - ๐งโ๐ซ 60 Implementations/tutorials of deep learning papers with side-by-side notes ๐; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ๐ฎ reinforcement learning (ppo, dqn), capsnet, distillation, ... ๐ง
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
guildai - Experiment tracking, ML developer tools
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.
Practical_RL - A course in reinforcement learning in the wild
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Deep-Learning-Push-Up-Counter - Deep Learning approach to count the number of repetitions in a video of push ups or pull ups.
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.
MIRNet-TFJS - TensorFlow JS models for MIRNet for low-light๐ก image enhancement
infery-examples - A collection of demo-apps and inference scripts for various deep learning frameworks using infery (Python).
Lottery_Ticket_Hypothesis-TensorFlow_2 - Implementing "The Lottery Ticket Hypothesis" paper by "Jonathan Frankle, Michael Carbin"
toucan-track - Achieving Full Body Tracking for VRChat using 2 cameras and machine learning.